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Record W2318830867 · doi:10.3896/ibra.1.52.1.03

Standard methods for estimating strength parameters of Apis mellifera colonies

2013· article· en· W2318830867 on OpenAlexaff
Keith S. Delaplane, Jozef van der Steen, Ernesto Guzmán‐Novoa

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiologyBroodDroneLongevityProxy (statistics)StatisticsEcologyMathematicsGenetics

Abstract

fetched live from OpenAlex

This paper covers measures of field colony strength, by which we mean population measures of adult bees and brood. There are generally two contexts in which an investigator wishes to measure colony strength: 1. at the beginning of a study as part of manipulations to produce uniform colonies and reduce experimental error and; 2. as response variables during or at the end of an experiment. Moreover, there are two general modes for measuring colony strength: 1. an objective mode which uses empirical measures and; 2. a subjective mode that relies on visual estimates by one or more observers. There is a third emerging mode for measuring colony strength; 3. computer-assisted digital image analysis. A final section deals with parameters that do not directly measure colony strength yet give important indicators of colony state: flight activity at the entrance; comb construction; and two proxy measures of colony fitness: production of queen cells and drone brood. How to conduct different types of experiments are described, including infectious dose, dose effects, course of infection and longevity tests.

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)0.019
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: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.008

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 head0.055
GPT teacher head0.289
Teacher spread0.233 · 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
GenreMethods

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

Citations257
Published2013
Admission routes1
Has abstractyes

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