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Record W2895399771 · doi:10.1093/jme/tjy173

Effect of Temperature on Oviposition Behavior and Egg Load of Blow Flies (Diptera: Calliphoridae)

2018· article· en· W2895399771 on OpenAlexafffundabout
Krystal R. Hans, R LeBouthillier, Sherah L. VanLaerhoven

Bibliographic record

VenueJournal of Medical Entomology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsUniversity of Windsor
FundersUniversity of WindsorGovernment of OntarioOntario Innovation Trust
KeywordsBiologyCalliphoridaePhormia reginaCarrionLuciliaZoologyLarvaEcologyEphemeral keyForagingReproductionAnimal science

Abstract

fetched live from OpenAlex

Making optimal oviposition decisions is especially important for female carrion colonizing insects whose larvae often depend on ephemeral resources. Optimal oviposition theory predicts that females should exhibit behaviors that will maximize the performance of their offspring. Oviposition can be influenced by a variety of factors, including temperature. The aim of this study was to investigate the effects of temperature on the oviposition behavior and egg load of two blow fly species native to southern Ontario: Phormia regina Meigen, and Lucilia sericata Meigen (Diptera: Calliphoridae). Using fetal pig carcasses as an oviposition substrate, we assessed the length of time until the first oviposition event, preferred oviposition sites, and the total number of eggs oviposited. For each of the two species, five different temperature treatments ranging from 15 to 35°C were used. Temperature influenced time to the first oviposition event, as well as the number of eggs deposited by L. sericata and P. regina. As temperature increased, oviposition occurred faster with more eggs deposited for these two species. Female P. regina, in particular, demonstrated a higher egg load with increasing temperature. Differences in optimal temperatures for reproduction, including egg load and oviposition, may be a mechanism driving seasonal temporal resource partitioning leading to species coexistence utilizing ephemeral resources.

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)0.001
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 score0.469
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.268
Teacher spread0.260 · 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

Citations24
Published2018
Admission routes3
Has abstractyes

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