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Record W2297711936 · doi:10.1111/eea.12425

Replacing qualitative life‐history traits by quantitative indices in parasitoid evolutionary ecology

2016· article· en· W2297711936 on OpenAlexaff
Guy Boivin, Jacintha Ellers

Bibliographic record

VenueEntomologia Experimentalis et Applicata · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyLife history theoryTraitEcologyEvolutionary ecologyLife historyParasitoidEvolutionary biologyHymenopteraHost (biology)

Abstract

fetched live from OpenAlex

Abstract Life‐history traits, which describe the various aspects of the life cycle of a species, can be either quantitative or qualitative. Quantitative traits are likely to adapt to gradual changes in the environment of a species, whereas qualitative traits, which refer to traits that are discontinuous in their variation, pose constraints on the evolution of a species. Traits that are described as qualitative may indeed represent discontinuous characteristics or they can be the result of an oversimplification in the description of the life history of a species. The ovigeny index, which describes the temporal distribution of egg production for a species, has replaced a qualitative life‐history trait and has been an important contribution in our understanding of the reproductive ecology of insect parasitoids. We propose here that several other qualitative life‐history traits, currently used to describe the evolutionary ecology of insect parasitoids, could advantageously be replaced by quantitative life‐history traits. Although replacing these qualitative life‐history traits will require devising indices that are biologically and ecologically meaningful, the potential gain in our understanding of the evolutionary forces that have shaped the diversity of life‐history strategies of insect parasitoids is important and would fully warrant this effort.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.472

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 head0.090
GPT teacher head0.319
Teacher spread0.229 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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