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Record W6893729052 · doi:10.5281/zenodo.4711447

Parallel and non-parallel divergence within polymorphic populations of brook stickleback, Culaea inconstans (Actinopterygii: Gasterosteidae)

2021· other· en· W6893729052 on OpenAlexaff

Bibliographic record

VenueOpen MIND · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsMount Royal University
Fundersnot available
KeywordsSticklebackParallel evolutionDivergence (linguistics)GasterosteusSPINE (molecular biology)Adaptation (eye)Biological evolution

Abstract

fetched live from OpenAlex

Studying parallel evolution allows us to draw conclusions about the repeatability of adaptive evolution. Whereas populations likely experience similar selective pressures in similar environments, it is not clear if this will always result in parallel divergence of ecologically relevant traits. Our study investigates the extent of parallelism associated with the evolution of pelvic spine reduction in brook stickleback populations. We find that populations with parallel divergence in pelvic spine morphology do not exhibit parallel divergence in head and body morphology but do exhibit parallel divergence in diet. In addition, we compare these patterns associated with pelvic reduction in brook stickleback to well-studied patterns of divergence between spined and unspined threespine stickleback. Whereas spine reduction is associated with littoral habitats and a benthic diet in threespine stickleback, spine reduction in brook stickleback is associated with a planktonic diet. Hence, we find that pelvic spine divergence is associated with largely non-parallel ecological consequences across 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 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.057
GPT teacher head0.311
Teacher spread0.254 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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