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Conservation Physiology

2020· book· en· W4230787387 on OpenAlexfundno aff

Bibliographic record

VenueOxford University Press eBooks · 2020
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
FundersNational Marine Fisheries ServiceOffice of Naval ResearchNational Oceanic and Atmospheric AdministrationUniversity of British ColumbiaMote Marine Laboratory and AquariumNorth Pacific Research BoardUniversity of Massachusetts DartmouthDartmouth CollegeNational Science Foundation
KeywordsScope (computer science)ToolboxDiversity (politics)WildlifeConservation biologyConservation psychologyWildlife conservationEnvironmental resource managementEcologyEnvironmental planningGeographyComputer scienceBiologyBiodiversityPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

Conservation physiology is a rapidly expanding, multi-disciplinary field that uses physiological tools to characterize and solve conservation problems. This text provides a consolidated overview of the scope, purpose, and goals of conservation physiology, with a focus on animals. It outlines the major avenues by which conservation physiology is contributing to the monitoring, management, and restoration of animal populations and defines opportunities for growth in the field. By using a series of case studies, it illustrates how approaches from the conservation physiology toolbox tackle diverse conservation issues ranging from monitoring environmental stress, predicting the impact of climate change, understanding disease dynamics, improving captive breeding, reducing human–wildlife conflict, and many others. Moreover, by acting as practical road maps across a diversity of subdisciplines, these case studies will serve to increase the accessibility of this discipline to new researchers. The diversity of taxa, biological scales, and ecosystems that are highlighted illustrate the far-reaching nature of the discipline and allow readers to gain an appreciation for the purpose, value, and status of the field.

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 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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.497
Threshold uncertainty score0.498

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 head0.018
GPT teacher head0.173
Teacher spread0.155 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations29
Published2020
Admission routes1
Has abstractyes

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