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Record W2734914907 · doi:10.1002/fee.1502

Behavioral flexibility as a mechanism for coping with climate change

2017· review· en· W2734914907 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2017
Typereview
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFlexibility (engineering)Climate changeEcologyAdaptive capacityBehavioral ecologyCoping (psychology)Resource (disambiguation)Environmental resource managementEnvironmental changePsychologyBiologyEnvironmental scienceComputer scienceEconomics

Abstract

fetched live from OpenAlex

Of the primary responses to contemporary climate change – “move, adapt, acclimate, or die” – that are available to organisms, “acclimate” may be effectively achieved through behavioral modification. Behavioral flexibility allows animals to rapidly cope with changing environmental conditions, and behavior represents an important component of a species’ adaptive capacity in the face of climate change. However, there is currently a lack of knowledge about the limits or constraints on behavioral responses to changing conditions. Here, we characterize the contexts in which organisms respond to climate variability through behavior. First, we quantify patterns in behavioral responses across taxa with respect to timescales, climatic stimuli, life‐history traits, and ecology. Next, we identify existing knowledge gaps, research biases, and other challenges. Finally, we discuss how conservation practitioners and resource managers can incorporate an improved understanding of behavioral flexibility into natural resource management and policy decisions.

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.

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)0.001
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 score0.990
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.092
GPT teacher head0.329
Teacher spread0.238 · 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