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Record W6931579763 · doi:10.5281/zenodo.5140203

Data from: Rearing and sampling methods for estimating spruce budworm development rates at constant temperatures

2021· other· en· W6931579763 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typeother
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSpruce budwormLarvaInstarConstant (computer programming)Protocol (science)

Abstract

fetched live from OpenAlex

We describe an experimental protocol for measuring the response of spruce budworm post-diapause larval development to temperature. This protocol is specifically designed to include measurements of development near their upper and lower thermal thresholds. The application of this protocol to a laboratory colony allowed for the first experimental evidence that spruce budworm larval development occurs at temperatures as low as 5 ºC and as high as 35 ºC and provides data to estimate development rates at temperatures from 5–35 ºC in 5 ºC increments. Our protocol is also designed to minimize mortality near the thermal development thresholds thus allowing for multi-generational studies. We observed developmental plasticity in larvae reared at constant temperatures, particularly the occurrence of up to 42% of some individuals requiring only five instars to complete development, compared to the expected six instars. An occurrence that exhibited no clear relation to temperature. While this protocol is specifically designed for spruce budworm, it provides a template for the study of other species' developmental responses to temperature.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.016

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 head0.096
GPT teacher head0.351
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicFlood Risk Assessment and ManagementFrench-language works237,207