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Comparative Psychoneuroimmunology/Ecoimmunology: Lessons from Simpler Model Systems

2012· book-chapter· en· W2622900474 on OpenAlexaff
Shelley A. Adamo

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychoneuroimmunologyImmune systemFunction (biology)PsychologyNeuroscienceAnorexiaImmunologyBiologyMedicineEvolutionary biology

Abstract

fetched live from OpenAlex

Abstract Immune-behavioral interactions are widespread throughout the animal kingdom. For example, decreased feeding after immune activation is common in animals. Work with insects suggests that changes in feeding behavior during an immune response (e.g., illness-induced anorexia) may be a behavioral method of biasing multifunctional physiological pathways toward immune function. Work on insects also suggests that stress hormones help to reconfigure the immune system in order to optimize its performance during the physiological shifts required for “flight-or-fight.” The effects of stress hormones on immune function appear maladaptive only when compared to what the animal could do under optimal conditions. Work with insects also cautions against overly simplistic interpretations of immune assay results. A comparative approach to psychoneuroimmunology will increase our understanding of the adaptive function of immune-behavioral interactions. Understanding why these connections exist is of both practical and theoretical importance.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.144
GPT teacher head0.304
Teacher spread0.160 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations7
Published2012
Admission routes1
Has abstractyes

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