To Stress or Not to Stress: A Question of Models
Bibliographic record
Abstract
Stress research is a rapidly evolving field that encompasses numerous disciplines ranging from neuroscience to metabolism. With many new researchers migrating into the field, navigating the hows and whys of specific research questions can sometimes be enigmatic given the availability of so many models in the stress field. Additionally, as with every field, there are many seemingly minor experimental details that can have dramatic influences on data interpretation, although many of these are unknown to those not familiar with the field. The aim of this overview is to provide some suggestions and points to guide researchers moving into the stress field and highlight relevant methodological points that they should consider when choosing a model for stress and deciding how to structure a study. We briefly provide a primer on the basics of endpoint measurements in the stress field, factors to consider when choosing a model for acute stress, the difference between repeated and chronic stress, and importantly, influencing variables that modulate endpoints of analysis in stress work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".