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Record W2143344601 · doi:10.1002/neu.10173

Different data from different labs: Lessons from studies of gene–environment interaction

2002· review· en· W2143344601 on OpenAlexaffabout
Douglas Wahłsten, Pamela Metten, Tamara J. Phillips, Stephen L. Boehm, Sue Burkhart‐Kasch, Janet Dorow, Sharon Doerksen, Chris Downing, Jennifer Fogarty, Kristina Rodd‐Henricks, René Hen, Carrie S. McKinnon, Catherine M. Merrill, Cedar Nolte, Melike Schalomon, Jason P. Schlumbohm, Jason R. Sibert, Charlotte D. Wenger, Bruce C. Dudek, John C. Crabbe

Bibliographic record

VenueJournal of Neurobiology · 2002
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsUniversity of Alberta
FundersNational Institute on Alcohol Abuse and AlcoholismStrong
KeywordsLocomotor activityPreferenceWater bottleGenotypeBiologyPsychologyDevelopmental psychologyNeuroscienceGeneticsGeneStatisticsPharmacologyBottle

Abstract

fetched live from OpenAlex

It is sometimes supposed that standardizing tests of mouse behavior will ensure similar results in different laboratories. We evaluated this supposition by conducting behavioral tests with identical apparatus and test protocols in independent laboratories. Eight genetic groups of mice, including equal numbers of males and females, were either bred locally or shipped from the supplier and then tested on six behaviors simultaneously in three laboratories (Albany, NY; Edmonton, AB; Portland, OR). The behaviors included locomotor activity in a small box, the elevated plus maze, accelerating rotarod, visible platform water escape, cocaine activation of locomotor activity, and ethanol preference in a two-bottle test. A preliminary report of this study presented a conventional analysis of conventional measures that revealed strong effects of both genotype and laboratory as well as noteworthy interactions between genotype and laboratory. We now report a more detailed analysis of additional measures and view the data for each test in different ways. Whether mice were shipped from a supplier or bred locally had negligible effects for almost every measure in the six tests, and sex differences were also absent or very small for most behaviors, whereas genetic effects were almost always large. For locomotor activity, cocaine activation, and elevated plus maze, the analysis demonstrated the strong dependence of genetic differences in behavior on the laboratory giving the tests. For ethanol preference and water escape learning, on the other hand, the three labs obtained essentially the same results for key indicators of behavior. Thus, it is clear that the strong dependence of results on the specific laboratory is itself dependent on the task in question. Our results suggest that there may be advantages of test standardization, but laboratory environments probably can never be made sufficiently similar to guarantee identical results on a wide range of tests in a wide range of labs. Interpretations of our results by colleagues in neuroscience as well as the mass media are reviewed. Pessimistic views, prevalent in the media but relatively uncommon among neuroscientists, of mouse behavioral tests as being highly unreliable are contradicted by our data. Despite the presence of noteworthy interactions between genotype and lab environment, most of the larger differences between inbred strains were replicated across the three labs. Strain differences of moderate effects size, on the other hand, often differed markedly among labs, especially those involving three 129-derived strains. Implications for behavioral screening of targeted and induced mutations in mice are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.040
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.066
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0050.003
Science and technology studies0.0020.014
Scholarly communication0.0030.007
Open science0.0060.006
Research integrity0.0020.011
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.233
GPT teacher head0.368
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainReproducibility
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations507
Published2002
Admission routes2
Has abstractyes

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