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Record W4402970582 · doi:10.1101/2024.09.27.615542

Roadkill is a crucial factor in the population decline of migratory monarch butterflies

2024· preprint· en· W4402970582 on OpenAlexafffund
Iman Momeni‐Dehaghi, Lenore Fahrig, Greg W. Mitchell, Trina Rytwinski, Jeffrey O. Hanson, Joseph Bennett

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsEnvironment and Climate Change CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaNature Conservancy of Canada
KeywordsMonarch butterflyPopulationGeographyBiologyEcologyDemographySociology

Abstract

fetched live from OpenAlex

Abstract The charismatic migratory monarch butterfly population has declined dramatically, likely precipitated by loss of its breeding host plants (milkweed). Whether restoring milkweed would allow monarch recovery depends on whether additional factors currently limit the population. We investigated road mortality as one such factor. Monarchs cross thousands of roads during fall migration, and traffic volume has increased sharply while the population has plummeted. Using estimates of pre-migration distribution, flight patterns, and road traffic, we estimate that at least 61% (range 61% to 99.99%) of migrating monarchs are road-killed each fall. Although there is high uncertainty in our estimate, its magnitude suggests that roadkill could inhibit recovery of the population. Recovery planning should not only consider increasing the monarch’s host plants, but must also address the reality of roadkill.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.025
GPT teacher head0.248
Teacher spread0.223 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

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