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Record W2165633997 · doi:10.1139/z04-064

Modeled climate change effects on distributions of Canadian butterfly species

2004· article· en· W2165633997 on OpenAlexvenueaboutno aff
A. Townsend Peterson, Enrique Martínez‐Meyer, Constantino González‐Salazar, Peter Hall

Bibliographic record

VenueCanadian Journal of Zoology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsClimate changeBiodiversityButterflyEcologyDanausFutures contractGeneral Circulation ModelClimate modelEnvironmental niche modellingDownscalingBiologyEnvironmental resource managementEcological nicheEnvironmental scienceHabitat

Abstract

fetched live from OpenAlex

Climate change effects on biodiversity are being documented now frequently in the form of changes in phenology and distributional shifts. However, the form that these effects will take over a longer timespan is unclear; for this understanding, a quantitative, validated, predictive approach is key. Here, we use ecological niche modeling and general circulation model outputs to estimate future potential geographic distributions of 111 Canadian butterfly species. We develop future estimates under two emission scenarios from each of two climate change modeling centers; future projections for biodiversity are not only scenario dependent (more severe emission scenarios produce more severe effects on species' distributions) but also model dependent (the Canadian Centre for Climate Modelling and Analysis results were more severe than the Hadley Centre results). One interesting feature is the appearance of disjunctions in species' distributions, hence creating "vicariant events" over very short time periods. In general, however, a cost of 1%–3% additional loss of species' distributions is associated with more severe scenarios of emissions and climate change, suggesting that subtle biodiversity consequences are associated with the different climate futures debated in political circles.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.991

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)0.0090.000

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 head0.030
GPT teacher head0.220
Teacher spread0.190 · 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

Citations74
Published2004
Admission routes2
Has abstractyes

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