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Record W3205591275 · doi:10.1163/1568539x-bja10132

Can animals develop depression? An overview and assessment of ‘depression-like’ states

2021· article· en· W3205591275 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBehaviour · 2021
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
FundersBiotechnology and Biological Sciences Research Council
KeywordsAnhedoniaDepression (economics)PsychologyNarrative reviewPleasureAnimal models of depressionAnimal welfareClinical psychologyPsychiatryPsychotherapistAnxietyBiologyAntidepressantEcology

Abstract

fetched live from OpenAlex

Abstract Describing certain animal behaviours as ‘depression-like’ or ‘depressive’ has become common across several fields of research. These typically involve unusually low activity or unresponsiveness and/or reduced interest in pleasure (anhedonia). While the term ‘depression-like’ carefully avoids directly claiming that animals are depressed, this narrative review asks whether stronger conclusions can be legitimate, with animals developing the clinical disorder as seen in humans (cf., DSM-V/ICD-10). Here, we examine evidence from animal models of depression (especially chronically stressed rats) and animals experiencing poor welfare in conventional captive conditions (e.g., laboratory mice and production pigs in barren environments). We find troubling evidence that animals are indeed capable of experiencing clinical depression, but demonstrate that a true diagnosis has yet to be confirmed in any case. We thus highlight the importance of investigating the co-occurrence of depressive criteria and discuss the potential welfare and ethical implications of animal depression.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.119
GPT teacher head0.420
Teacher spread0.301 · 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