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Record W3094461789 · doi:10.1139/cjz-2020-0152

Maternal body size and age govern reproduction and offspring phenotype in the zig-zag ladybird beetle (<i>Menochilus sexmaculatus</i>)

2020· article· en· W3094461789 on OpenAlexvenueno aff
Shashwat Singh, Geetanjali Mishra, Omkar Omkar

Bibliographic record

VenueCanadian Journal of Zoology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersUniversity Grants Commission
KeywordsBiologyOffspringMatingReproductionZoologyLarvaMaternal effectPredationEcologyPregnancyGenetics

Abstract

fetched live from OpenAlex

Maternal effects are possible channels through which mothers provision their offspring differentially, thereby affecting offspring phenotype. We investigated maternal effects in the zig-zag ladybird beetle (Menochilus sexmaculatus (Fabricius, 1781) = Cheilomenes sexmaculata (Fabricius, 1781)) in response to body size (induced by different feeding regimes during larval development) and their age within the reproductive cycle. Different-sized females were permitted to mate and were provided with daily-replenished ad libitum prey. After mating, reproductive output and developmental duration of offspring from different oviposition days were recorded. We hypothesized that small females would lay smaller and fewer eggs than larger females, and that egg mass would also reduce with increased maternal age. In our study, the larger mothers laid more eggs per day. Small and large mothers oviposited maximally at middle age. Maternal age did not influence the egg mass, although it was slightly higher in the case of older, larger females. Offspring from old, small and large mothers developed rapidly. This nimble development could be an adaptive strategy for the use of ephemeral aphid patches. The results of the study are indicative of this ladybird species’ ability to adjust their offspring’s life-history traits, a feature more prominent in larger females.

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 score0.434
Threshold uncertainty score0.995

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 head0.010
GPT teacher head0.188
Teacher spread0.177 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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