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Record W4399543186 · doi:10.3389/fevo.2024.1384291

Turning setbacks into stepping-stones for growth in conservation paleobiology

2024· article· en· W4399543186 on OpenAlexaff
G. Lynn Wingard, Chris L. Schneider, Gregory P. Dietl, Damien A. Fordham

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConservation biologyPaleobiologyEnvironmental resource managementBiodiversityConservationEcosystem managementResource (disambiguation)Conservation psychologyRestoration ecologyEcosystemEcologyEnvironmental planningGeographyComputer scienceBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Conservation paleobiology is a cross-disciplinary field that utilizes the geohistorical record of past life on Earth to inform present-day decisions in conservation and restoration and assist in planning for future natural resource management. However, information on how past ecosystems and species responded to environmental change over decadal to millennial timescales is rarely incorporated into conservation and restoration decision-making. To heighten awareness among conservation and restoration practitioners of the relevance of geohistorical data and to bridge the gap between research and implementation in conservation paleobiology, we proposed a Research Topic titled “ Integrating Conservation Biology and Paleobiology to Manage Biodiversity and Ecosystems in a Changing World ”. The 21 articles subsequently published demonstrate the diversity and breadth of geohistorical information available to resource management and the challenges of translating these results into conservation practice and policy. Here we discuss the lessons we learned from editing the Research Topic and suggest a pathway forward for conservation paleobiologists who aspire to generate actionable research results to solve current problems in biodiversity conservation and ecological restoration.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.337

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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