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Record W4283325867 · doi:10.1126/science.abo3608

Getting ahead of climate change for ecological adaptation and resilience

2022· review· en· W4283325867 on OpenAlexaff
Jonathan W. Moore, Daniel E. Schindler

Bibliographic record

VenueScience · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsResilience (materials science)Environmental resource managementClimate changeAdaptation (eye)Stewardship (theology)Psychological resilienceBiodiversityAdaptive managementBiosphereEnvironmental planningEcologyBusinessGeographyEnvironmental sciencePolitical scienceBiology

Abstract

fetched live from OpenAlex

Changing the course of Earth's climate is increasingly urgent, but there is also a concurrent need for proactive stewardship of the adaptive capacity of the rapidly changing biosphere. Adaptation ultimately underpins the resilience of Earth's complex systems; species, communities, and ecosystems shift and evolve over time. Yet oncoming changes will seriously challenge current natural resource management and conservation efforts. We review forward-looking conservation approaches to enable adaptation and resilience. Key opportunities include expanding beyond preservationist approaches by including those that enable and facilitate ecological change. Conservation should not just focus on climate change losers but also on proactive management of emerging opportunities. Local efforts to conserve biodiversity and generate habitat complexity will also help to maintain a diversity of future options for an unpredictable future.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.003

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 head0.181
GPT teacher head0.359
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations187
Published2022
Admission routes1
Has abstractyes

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