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Record W2794115523 · doi:10.1093/conphys/coy015

Crossing boundaries in conservation physiology

2018· editorial· en· W2794115523 on OpenAlexafffund
Sean Tomlinson, Jodie L. Rummer, Kevin R. Hultine, Steven J. Cooke

Bibliographic record

VenueConservation Physiology · 2018
Typeeditorial
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsCarleton University
FundersAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiologyEcologyPhysiology

Abstract

fetched live from OpenAlex

Ecophysiology investigates the mechanisms underpinning the interactions between an organism and its environment (Block and Vannier, 1994). The audience of, and contributors to, ‘Conservation Physiology’ are well aware as to how intriguingly complex these interactions can be; yet, we tend to take this one step further. We do not aim just to understand these complexities in some abstract sense. We want our research to be ‘useful and relevant’ to society. A critical aspect of ecophysiology has always been that it can be researched in anthropogenic or managed environments (Feder and Block, 1991; Block and Vannier, 1994), and there is a long heritage that enmeshes physiological measurements with conservation interests (Wikelski and Cooke, 2006; Cooke et al., 2013). Indeed, it has been recognized for well over two decades that the full potential of conservation science depends on the integration of diverse expertise (Cooke et al., 2013), and between research and practice (Cooke and O’Connor, 2010). Yet is conservation physiology really a useful science, and have we truly begun to establish the levels of cross-disciplinary collaboration required to make it so? Furthermore, do these broad collaborations involving complex research themes fit within the practical urgency of a conservation crisis?

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.262
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations6
Published2018
Admission routes2
Has abstractyes

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