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Record W1480869617 · doi:10.1111/afe.12083

Patterns of diapause frequency and emergence in swede midges of southern Ontario

2014· article· en· W1480869617 on OpenAlexafffundabout
Lauren E. Des Marteaux, Jonathan M. Schmidt, Marc Habash, Rebecca H. Hallett

Bibliographic record

VenueAgricultural and Forest Entomology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of GuelphWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsDiapauseBiologyMidgephotoperiodismVoltinismPEST analysisLarvaGrowing seasonEcologyZoologyHorticulture

Abstract

fetched live from OpenAlex

Abstract The swede midge is an invasive pest of crucifers in North America and exhibits temporal plasticity in diapause; diapause frequencies change throughout the growing season and spring emergence is typically bimodal. Factors controlling the timing of swede midge diapause events are not well understood. Pre‐diapause larval swede midge populations were isolated within cages in the field and tracked for emergence over 3 years. Diapause frequency was inversely correlated with photoperiod and absolute maximum air temperature, however photoperiod did not influence emergence timing. Emergence from diapause occurred in two large peaks, in mid‐June and early July, with a third, smaller peak in late August. Emergence phenotypes may correspond to different diapause durations from 236 to 296 days, or post‐diapause development requiring between 516 and 1449 degree days. Approximately 2% of swede midges overwintered for 2 years. Early control efforts would be most effective if they targeted the diapausing cohort ( i.e. adults emerging June to July) to prevent damage by subsequent generations. Although few individuals overwintered for 2 years, prolonged diapause should be considered when crop rotation is employed for swede midge management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.964

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)0.0000.000

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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.

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

Citations12
Published2014
Admission routes3
Has abstractyes

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