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Record W6929244452 · doi:10.5061/dryad.vq83bk440

Data from: Individual variation and selection for phenotypic plasticity of laying date and clutch size across different environmental conditions in Tree swallow (Tachycineta bicolor)

2024· dataset· en· W6929244452 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2024
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPhenotypic plasticityAvian clutch sizeSelection (genetic algorithm)PlasticityPopulationVariation (astronomy)Adaptation (eye)Tree (set theory)

Abstract

fetched live from OpenAlex

Several studies have emphasized that phenotypic plasticity should be a key mechanism to cope with current rapid environmental changes by allowing individuals to quickly express new adaptive phenotypes. Yet, few studies have investigated the evolutionary potential of plasticity for multiple traits simultaneously and using several different environmental variables. Here, we assess the extent of variation in, and the selection acting on phenotypic plasticity of key ecological traits, laying date and clutch size, using five environmental variables, in a Tree swallow (Tachycineta bicolor) population monitored since 2004. While we found some variation among females in their mean laying date and plasticity, we found evidence of selection acting only on mean laying date. We found no variation among females in mean clutch size or plasticity, such that we could not assess selection acting on either. Our results suggest that the evolutionary potential of plasticity in the population under study is limited, especially for clutch size. More studies investigating plasticity in wild populations and incorporating multiple traits and environmental variables are needed to understand future responses of animal populations to environmental changes.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.507
Threshold uncertainty score0.633

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 science0.0000.001
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 head0.031
GPT teacher head0.308
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2024
Admission routes1
Has abstractyes

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