Multisystem Regulation of Performance Deficits Induced by Stressors: An Animal Model of Depression
Bibliographic record
Abstract
When an organism is exposed to a stressor, a series of behavioral changes occur that are thought to be of adaptive value. Among other things, the response style of an organism will narrow to those innate responses highest in the animal’s defensive repertoire ( see belles, 1970) or to responses previously acquired in aversive situations. In addition, several neurochemical changes occur that may blunt the physical or psychological impact of the stressor, increase arousal or vigilance, or increase the animal’s ability to initiate and sustain defensive responses ( see reviews in Zacharko and Anisman, 1989; Maier and Seligman, 1976; Weiss and Simson, 1985). However, there maybe occasions where these responses may have adverse consequences. For instance, when the response required to escape from the stressor is not part of the organism’s repertoire, the persistent adoption of these response styles may be counterproductive. Likewise, excessive utilization may reduce neurotransmitter stores, rendering the animal less able to deal with environmental demands. It has been our contention that many of the behavioral and physiological disturbances associated with acute and chronic uncontrollable stressors stem from the failure of adaptive neurochemical mechanisms. This chapter will outline some of the biochemical and behavioral consequences of stressors, particularly as they relate to an animal model of depression. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".