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Record W4226097086 · doi:10.1093/conphys/coac015

Elevating the impact of conservation physiology by building a community devoted to excellence, transparency, ethics, integrity and mutual respect

2022· article· en· W4226097086 on OpenAlexaff
Steven J. Cooke, Kevin R. Hultine, Jodie L. Rummer, Nann A. Fangue, Frank Seebacher, Erika J. Eliason, Heath A. MacMillan, Andrea Fuller, Craig E. Franklin

Bibliographic record

VenueConservation Physiology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Ecology, Wildlife Education
Canadian institutionsCarleton University
Fundersnot available
KeywordsTransparency (behavior)BiologyExcellenceEngineering ethicsEnvironmental ethicsEthical standardsEnvironmental resource managementEnvironmental planningAccountingBusinessPolitical scienceLawEngineeringEconomics

Abstract

fetched live from OpenAlex

[Extract] Ten years ago, the journal Conservation Physiology was launched jointly by the Society for Experimental Biology and Oxford University Press. Much has been accomplished since 2012 including publishing over 600 papers in the journal and helping to build a sense of place for aspiring and practicing conservation physiologists (Cooke et al., 2020). Yet, more work is needed to further elevate the impact of conservation physiology as a discipline and community. Here, we summarize what is needed to build and strengthen a community devoted to not only excellence, transparency, ethics, integrity and mutual respect, but also courage to tackle some of the overarching challenges humanity faces. As active voices in the conservation physiology community we hope that this paper will help shape the future of our discipline while also guiding the activities and priorities of the journal and editorial team. Since the term ‘conservation physiology’ was coined by Wikelski and Cooke (2006) it has emerged as an essential component of conservation science and practice. Conservation physiology is about the use of physiological tools, knowledge and concepts to understand and solve conservation problems across diverse taxa (Cooke et al., 2013). It is regarded as being particularly effective at understanding mechanisms, generating cause–effect relationships (e.g. threat X does Y to organism Z), creating predictive tools and testing conservation interventions (Cooke and O’Connor, 2010). Issues relevant to conservation physiology range from very local, focused on recovery of an imperilled population (Birnie-Gauvin et al., 2017), to global-scale issues such as tackling the UN Sustainable Development Goals (Cooke et al., 2020) and the climate crisis (Madliger et al., 2021c). The discipline is now supported by a conceptual framework (Coristine et al., 2014), a journal (https://academic.oup.com/conphys) and a reference book (Madliger et al. 2021a). There is also a growing community of researchers who engage in conservation physiology and even define themselves as conservation physiologists (Madliger et al., 2021b). Moreover, in conservation physiology there are success stories that demonstrate the potential of conservation physiology (Madliger et al., 2016).

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.334
Teacher spread0.278 · 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.

Study designObservational
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

Citations3
Published2022
Admission routes1
Has abstractyes

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