MétaCan
Menu
Back to cohort
Record W2751543007 · doi:10.1111/een.12471

Estimating consumable biomass from body length and order in insects and spiders

2017· article· en· W2751543007 on OpenAlexafffund
Samantha Straus, Leticia Avilés

Bibliographic record

VenueEcological Entomology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAllometryBiologyBiomass (ecology)ArthropodInsectivorePredationInsectSample size determinationEcologyAnimal scienceZoologyStatisticsMathematics

Abstract

fetched live from OpenAlex

1. Current models used to estimate insect prey biomass for diet studies use whole weight. However, a large proportion of an arthropod's body is taken up by an indigestible exoskeleton, leading to erroneous estimation of the food intake of insectivorous animals. 2. Linear mixed effect models were used to obtain equations to predict consumable biomass from body length for a variety of N eotropical insects and spiders. These data were obtained by feeding taxa of various orders to groups of 100 social spiders and comparing pre‐ and post‐consumption weights using size‐matched controls. 3. Significant linear relationships were found relating body size to consumed biomass for all orders, with slopes ranging from 1.276 to 4.011 and R 2 values from 0.476 to 0.929. For orders other than spiders and O rthoptera, the increase in weight with size exhibited negative allometric scaling, suggesting a decrease in tissue density, or an increase in internal air space, with size. 4. Although there were significant differences across taxonomic orders in the proportion of biomass consumed, within most orders the proportion consumed did not differ significantly with body size. The estimated regression coefficients may be used by other workers to estimate consumable biomass of arthropod prey for studies requiring large sample sizes or non‐lethal sampling of rare or endangered species.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.266
Teacher spread0.250 · 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

Citations14
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueEcological EntomologySame topicIsotope Analysis in EcologyFrench-language works237,207