Sentences with phrase «observation bias in»

«There is practically no time of observation bias in urban - based stations which have taken their measurements punctually always at the same time, while in the rural stations the times of observation have changed.

Not exact matches

And without a biased belief in induction and that you have some control on your variables your observations would not be predictive.

Even if hindsight bias allows us to point out all the cases where it has turned out to be a mistake — a mistake that sometimes delayed paradigm shifts in science for years or decades — it's still usually best to start by attempting to explain anomalous observations within the theoretical framework we have.

I get quite a lot of stick for being critical of referees in Arsenal games, and I'll be the first to admit that my observations are built on bias, so of course there is a good chance that people may disagree with my viewpoints, particularly when their biases lay elsewhere.

From our previous studies we have found that people are inaccurate in their food observations and are biased to notice certain food items over others.

Moreover, much related research does not rely on monkey studies, which may be particularly vulnerable to confirmation bias — the unwitting tendency to interpret observations in a way that fits preexisting beliefs.

However, scientists from the Canadian - French - Hawaiian project OSSOS detected biases in their own observations of the orbits of the TNOs, which had been systematically directed towards the same regions of the sky, and considered that other groups, including the Caltech group, may be experiencing the same issues.

FMI has been involved in research project, which evaluated the simulations of long - range transport of BB aerosol by the Goddard Earth Observing System (GEOS - 5) and four other global aerosol models over the complete South African - Atlantic region using Cloud - Aerosol Lidar with Orthogonal Polarization (CALIOP) observations to find any distinguishing or common model biases.

Some of the discontinuities (which can be of either sign) in weather records can be detected using jump point analyses (for instance in the new version of the NOAA product), others can be adjusted using known information (such as biases introduced because changes in the time of observations or moving a station).

The observation that Boule homologs show predominantly testis - biased expression in diverse species is consistent with a conserved male gametogenic function in bilateral animals.

Supporting this is our observation that approximately one third of both OR and VR genes with interrupted ORFs are not expressed in olfactory tissues, a bias that had been noted previously [41].

All data were reduced using the SOSIE algorithm, which accounts for systematic biases present in previously published observations.

Early adopter states have struggled with data integrity, inflated scores, and bias in classroom observations,» he wrote.

But the bias in classroom observation is not a serious problem with respect to teacher dismissal.

We demonstrated that a regression - based statistical correction for the proportion of the students in each teacher's class that are English - language learners, have education disabilities, are from low - income families, and so forth, wrings most of the bias out of classroom observations.

The bias in classroom observation systems that derives from some teachers being assigned much more able students than other teachers is very important to the overall performance of the teacher evaluation system.

But in the districts we examined, only teachers at the very tail end of the distribution are dismissed because of their evaluation scores, and it turns out that teachers who get the very worst evaluation scores remain at the tail end of the distribution regardless of whether their classroom observation ratings are biased.

In our report, we introduced a method for adjusting for the bias in classroom observation scores by taking into account the demographic make - up of teachers» classroomIn our report, we introduced a method for adjusting for the bias in classroom observation scores by taking into account the demographic make - up of teachers» classroomin classroom observation scores by taking into account the demographic make - up of teachers» classrooms.

Most importantly, we discovered that there is bias in the classroom observation scores due to student ability.

(If some teachers are assigned particularly engaged or cohesive classrooms year after year, the results could still be biased; this approach, however, does eliminate bias due to year - to - year differences in unmeasured classroom traits being related to classroom observation scores.)

As I noted two years ago in reviewing Education Sector's proposal, even the most - comprehensive site review will be just as inaccurate (and burdened with biases) in measuring how well schools are serving kids as observations and peer reviews.

Following - up on two prior posts about potential bias in teachers» observations (see prior posts here and here), another research study was recently released evidencing, again, that the evaluation ratings derived via observations of teachers in practice are indeed related to (and potentially biased by) teachers» demographic characteristics.

Teachers with students with higher incoming achievement levels receive classroom observation scores that are higher on average than those received by teachers whose incoming students are at lower achievement levels, and districts do not have processes in place to address this bias.

This is much different than just jumping in right away on our first observation of a price action signal or market bias.

In this case, there has been an identification of a host of small issues (and, in truth, there are always small issues in any complex field) that have involved the fidelity of the observations (the spatial coverage, the corrections for known biases), the fidelity of the models (issues with the forcings, examinations of the variability in ocean vertical transports etc.), and the coherence of the model - data comparisonIn this case, there has been an identification of a host of small issues (and, in truth, there are always small issues in any complex field) that have involved the fidelity of the observations (the spatial coverage, the corrections for known biases), the fidelity of the models (issues with the forcings, examinations of the variability in ocean vertical transports etc.), and the coherence of the model - data comparisonin truth, there are always small issues in any complex field) that have involved the fidelity of the observations (the spatial coverage, the corrections for known biases), the fidelity of the models (issues with the forcings, examinations of the variability in ocean vertical transports etc.), and the coherence of the model - data comparisonin any complex field) that have involved the fidelity of the observations (the spatial coverage, the corrections for known biases), the fidelity of the models (issues with the forcings, examinations of the variability in ocean vertical transports etc.), and the coherence of the model - data comparisonin ocean vertical transports etc.), and the coherence of the model - data comparisons.

And of course the new paper by Hausfather et al, that made quite a bit of news recently, documents how meticulously scientists work to eliminate bias in sea surface temperature data, in this case arising from a changing proportion of ship versus buoy observations.

Even with a near - perfect model and accurate observations, model - observation comparisons can show big discrepancies because the diagnostics being compared while similar in both cases, actually end up be subtly (and perhaps importantly) biased.

Some of the discontinuities (which can be of either sign) in weather records can be detected using jump point analyses (for instance in the new version of the NOAA product), others can be adjusted using known information (such as biases introduced because changes in the time of observations or moving a station).

However, my statistics experience would hesitate to call that a representative sample since most of the observations in my sample are Wesleyan students and would therefore have an element of bias.

A lot of the observation based estimates are likely biased low, as outlined in the Ringberg report just due to assumptions of linearity in the evolution of surface temperature in response to some given radiative nudge on the system.

Progress in the longer term depends on identifying and correcting model biases, accumulating as complete a set of historic observations as possible, and developing improved methods of detection and correction of observational biases.»

As they point out, «In reality, however, observational coverage varies over time, observations are themselves prone to bias, either instrumental or through not being representative of their wider surroundings, and these observational biases can change over time.

The initial picture presented by Marvel et al (founded on incorrect Fi value for CO2) was that the WMGHG results were basically in line with CO2 - only response, and the historical run was a low outlier — giving rise to the paper's argument that observation - derived TCR values would be biased low because of this «accident of history».

There are important implications in this observation not least the possibility of biased regression coefficients in attempts to reconstruct past low - frequency temperature change based on long density series calibrated against recent temperatures.

Unlike many data sets that have been used in past climate studies, these data have been adjusted to remove biases introduced by station moves, instrument changes, time - of - observation differences, and urbanization effects.

This change in observations makes the in situ tem - peratures up to about 0.1 °C cooler than they would be without bias.

Both theory and models predict the SIE in the Antarctic to decrease.That both CMIP5 and PMIP3 (paleo) fail to capture both observations (The spreads are in the Mkm ^ 2) and theoretical expectations suggest that systemic bias in the models.

I've seen a credible explanation for why, beginning in 1950, time of observation bias (TOBS) and station homogeneity (SHAP) became so skewed.

«The messages of the two points outlined in the extract above are: (1) the claims about increases in frequency and intensity of extreme events are generally not supported by actual observations and, (2) official information about climate science is largely controlled by agencies through (a) funding choices for research and (b) by the carefullyselected (i.e. biased) authorship of reports such as the EPA Endangerment Finding and the National Climate Assessment.»

However, these measurements contain non-negligible random errors and biases owing to the indirect nature of the relationship between the observations and actual precipitation, inadequate sampling, and deficiencies in the algorithms.

Surface warming / ocean warming: «A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets» «Estimating changes in global temperature since the pre-industrial period» «Possible artifacts of data biases in the recent global surface warming hiatus» «Assessing the impact of satellite - based observations in sea surface temperature trends»

So, for example, the conversion from Stevenson Screens to MMTS was accompanied by the observation of a «cooling bias» in the MMTS, which was «corrected».

«When initialized with states close to the observations, models «drift» towards their imperfect climatology (an estimate of the mean climate), leading to biases in the simulations that depend on the forecast time.

The issue of tropospheric temperature amplification remains to be completely resolved, but disparities between predictions and observations have diminished as instrument biases have been corrected, and it is not unreasonable to expect that further improvements in instrumentation accuracy will largely eliminate the remaining disparities.

A recent study by Cowtan et al. (paper here) suggests that accounting for these biases between the global temperature record and those taken from climate models reduces the divergence in trend between models and observations since 1975 by over a third.

In a recent paper published in Nature Communications, using both observations and a coupled Earth system model (GFDL - ESM2G) with a more realistic simulation of the Atlantic Meridional Overturning Circulation (AMOC) structure, and thus reduced mean state biases in the North Atlantic, the authors show that the decline of the Atlantic major hurricane frequency during 2005 — 2015 is associated with a weakening of the AMOC directly observed from the RAPID prograIn a recent paper published in Nature Communications, using both observations and a coupled Earth system model (GFDL - ESM2G) with a more realistic simulation of the Atlantic Meridional Overturning Circulation (AMOC) structure, and thus reduced mean state biases in the North Atlantic, the authors show that the decline of the Atlantic major hurricane frequency during 2005 — 2015 is associated with a weakening of the AMOC directly observed from the RAPID prograin Nature Communications, using both observations and a coupled Earth system model (GFDL - ESM2G) with a more realistic simulation of the Atlantic Meridional Overturning Circulation (AMOC) structure, and thus reduced mean state biases in the North Atlantic, the authors show that the decline of the Atlantic major hurricane frequency during 2005 — 2015 is associated with a weakening of the AMOC directly observed from the RAPID prograin the North Atlantic, the authors show that the decline of the Atlantic major hurricane frequency during 2005 — 2015 is associated with a weakening of the AMOC directly observed from the RAPID program.

MM04 failed to acknowledge other independent data supporting the instrumental thermometer - based land surface temperature observations, such as satellite - derived temperature trend estimates over land areas in the Northern Hemisphere (Intergovernmental Intergovernmental Panel on Climate Change, Third Assessment Report, Chapter 2, Box 2.1, p. 106) that can not conceivably be subject to the non-climatic sources of bias considered by them.

Yet in the paper he co-authored on the subject with Watts, Christy apparently did not know to take the first and most critical step of homogenizing the data and removing the climate - unrelated biases introduced by factors like stations moving and time of observation changing.

And in that sense, a climate model is nothing more or less than a conceptual model, necessary to give a frame of reference to the state of the real world, which can never be generated by observations alone (which are incomplete, may be inconsistent, contradicting each other, biased, non-representative,...).

«Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 1.