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Record W3147344610 · doi:10.1002/fee.2320

Trends in ecology and conservation over eight decades

2021· review· en· W3147344610 on OpenAlexaff
Sean C. Anderson, Paul R. Elsen, Brent B. Hughes, Rebecca K. Tonietto, Molly C. Bletz, David Gill, Meredith A. Holgerson, Sara E. Kuebbing, Caitlin McDonough MacKenzie, Mariah H. Meek, Diogo Veríssimo

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2021
Typereview
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsEcologyConservation biologyMultidisciplinary approachField (mathematics)BoomBustGeographyEnvironmental resource managementSociologySocial scienceBiologyEnvironmental science

Abstract

fetched live from OpenAlex

The fields of ecology and conservation have evolved rapidly over the past century. Synthesizing larger trends in these disciplines remains a challenge yet is critical to bridging subdisciplines, guiding research, and informing educational frameworks. Here, we provide what we believe is the largest full‐text culturomic analysis of ecology and conservation journals, covering 80 years, 52 journals, and half a billion words. Our analysis illuminates the boom‐and‐bust of ecological hypotheses and theories; the adoption of statistical, genetic, and social‐science approaches; and the domination of terms that have emerged in recent decades (eg climate change , invasive species , ecosystem services , meta‐analysis , and supplementary material, which largely replaced unpublished data ). We track the evolution of ecology from a largely descriptive field focused on natural history and observational studies to a more data‐driven, multidisciplinary field focused on applied environmental issues. Overall, our analysis highlights the increasing breadth of the field, illustrating that there is room for more diversity of ecologists and conservationists today than ever before.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.722
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations112
Published2021
Admission routes1
Has abstractyes

Explore more

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