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Record W2744593452 · doi:10.4039/tce.2017.31

Population variability of a native and an introduced herbivore of carrots (Apiaceae)

2017· article· en· W2744593452 on OpenAlexaff
Robert J. Lamb, Guy Boivin

Bibliographic record

VenueThe Canadian Entomologist · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCurculionidaeBiologyWeevilApiaceaePopulationAbundance (ecology)HerbivoreHabitatDaucus carotaBotanyEcology

Abstract

fetched live from OpenAlex

Abstract Long-term, twice weekly, trap catches of the native carrot weevil, Listronotus oregonensis (LeConte) (Coleoptera: Curculionidae), and the introduced carrot rust fly, Psila rosae (Fabricius) (Diptera: Psilidae), were used to test the hypothesis that native populations fluctuate less from year-to-year than those of introduced species, because the native species has had more time to adapt to temporal variability in its habitat than an introduced species. Variability in annual abundance was estimated for 33 years, and for 11-year or 16–17-year subsets of the 33-year time series. Temporal population variability was quantified as PV, a proportion between 0 and 1. The native carrot weevil had a PV of 0.39, less than that of the introduced carrot rust fly with a PV of 0.67, supporting the hypothesis. Generation 1 for both species showed a decline in PV over three decades consistent with the hypothesis that adaptation to variability in the habitat leads to lower PV. Over 33 years, the carrot weevil developed a second generation with a PV of 0.70, higher than that of the first generation, which is consistent with the hypothesis that adaptation is required to stabilise population dynamics in a new habitat, in this case a new temporally defined habitat.

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 score0.975
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.265
Teacher spread0.251 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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