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Dietary phosphorus availability influences female cricket lifetime reproductive effort

2010· article· en· W2011572332 on OpenAlexafffund
Laksanavadee Visanuvimol, Susan M. Bertram

Bibliographic record

VenueEcological Entomology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAchetaBiologyPhosphorusEcological stoichiometryCricketNutrientEcologyBiomass (ecology)Reproductive successHerbivoreZoology

Abstract

fetched live from OpenAlex

1. Recent ecological stoichiometric findings indicate that the relationships among key macronutrient elements [e.g. carbon (C), nitrogen (N), and phosphorus (P) of organisms and their resources] may underlie variation in life‐history traits. The amount of phosphorus in an individual's body is often correlated with its rate of growth, and low‐phosphorus diets are known to reduce growth in a number of insect and crustacean herbivores. 2. These findings suggest that the stoichiometric imbalance between organismal biomass requirements and the relative scarcity of nutrients in nature may also underlie variation in lifetime reproductive success. 3. This study investigated how dietary phosphorus availability during adulthood influenced lifetime reproductive effort, compensatory feeding, lifespan, condition, and stoichiometry of adult European House Cricket, Acheta domesticus . 4. Female crickets fed high amounts of phosphorus during adulthood laid significantly more eggs compared to those fed low amounts of phosphorus. Phosphorus availability did not directly influence lifespan, condition, or body stoichiometry, and crickets did not compensate for low phosphorus diets by eating more food. 5. A stoichiometric perspective may help understand the causes of variation in invertebrate fitness.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.024
GPT teacher head0.263
Teacher spread0.239 · 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 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

Citations32
Published2010
Admission routes2
Has abstractyes

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