MétaCan
Menu
Back to cohort
Record W3141722551 · doi:10.1017/s0007485321000201

Plasticity of nutrient accumulation patterns in diapausing fall webworm pupae

2021· article· en· W3141722551 on OpenAlexaff
Lvquan Zhao, Wei Wang, Ying Qiu, Alex S. Torson

Bibliographic record

VenueBulletin of Entomological Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsWestern University
Fundersnot available
KeywordsDiapauseBiologyphotoperiodismPupaOverwinteringLarvaBotany

Abstract

fetched live from OpenAlex

, diapause as overwintering pupae and their diapause incidence and diapause intensity are regulated by the photoperiod. In this study, we test the hypothesis that photoperiod influences energy reserve accumulation during diapause preparation in fall webworm. We found that the body size and mass, lipid and carbohydrate content of pupae with a short photoperiod during the diapause induction phase were significantly greater than those of pupae with a relatively short photoperiod, and the efficiency of converting digested food and ingested food into body matter was greater in the short-photoperiod diapause-destined larvae than the relatively short-photoperiod diapause-destined larvae. We also observed higher lipase and amylase activities in short-photoperiod diapause-destined larvae relative to the counterparts. However, no obvious difference was found in protein and protease in the pupae with a short photoperiod during the diapause induction phase and short-photoperiod diapause-destined larvae compared with the counterparts. Therefore, we conclude that the energy reserve patterns of diapausing fall webworm pupae are plastic and that short-photoperiod diapause-destined larvae increase their energy reserves by improving their feeding efficiency and increase their lipid and carbohydrate stores by increasing the lipase and amylase activities in the midgut.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.143
GPT teacher head0.373
Teacher spread0.230 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBulletin of Entomological ResearchSame topicPhysiological and biochemical adaptationsFrench-language works237,207