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Record W2113030628 · doi:10.4039/tce.2012.81

A method for induction and quantification of diapause entry in the swede midge (Diptera: Cecidomyiidae)

2012· article· en· W2113030628 on OpenAlexafffund
Lauren E. Des Marteaux, Marc Habash, Jonathan M. Schmidt, Rebecca H. Hallett

Bibliographic record

VenueThe Canadian Entomologist · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsDiapauseMidgeCecidomyiidaeBiologyPupaLarvaVoltinismDormancyBotanyHorticultureGermination

Abstract

fetched live from OpenAlex

Abstract Induction of diapause under laboratory conditions is a valuable tool for the study of dormancy in economic pests such as the swede midge, Contarinia nasturtii Kieffer (Diptera: Cecidomyiidae). In the present study, diapause in larval swede midge was achieved via manipulation of rearing photoperiod and temperature. Frequency of diapause was assessed by sieve separation of diapause cocoons from pre-sifted peat substrate following emergence of pupating individuals. Mean diapause frequency for swede midge larvae reared under cool conditions with short day length or cool conditions with decreasing day lengths were 45.2% and 19.5%, respectively. Only 1.2% of swede midge reared under warm, long day length conditions entered diapause. A small percentage of larvae neither pupated nor entered diapause and remained in substrate long after other individuals had emerged as adults. This behaviour was more prevalent under cool and short or decreasing day length rearing conditions. Approximately 76% of the larvae used for diapause induction were recovered with the present larval and cocoon retrieval method, and premature (larval and pupal) mortality averaged 18.2%. Although diapause occurred in the present study, conditions resulting in higher diapause frequencies should be investigated and attempts should be made to improve survival and recovery of individuals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.950

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 head0.054
GPT teacher head0.291
Teacher spread0.237 · 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

Citations14
Published2012
Admission routes2
Has abstractyes

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