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Record W1998258115 · doi:10.1139/z99-215

Shell shape, dysodont tooth morphology, and hinge-ligament thickness in the bay mussel <i>Mytilus trossulus</i> correlate with wave exposure

2000· article· en· W1998258115 on OpenAlexvenueaboutno aff
Robert J. Akester, André L. Martel

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMusselShell (structure)BayBiologyShoreMytilusRocky shoreMorphology (biology)FisheryZoologyGeologyOceanographyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

We examined the shell morphology of the bay mussel Mytilus trossulus (Gould) at six locations with various levels of wave exposure in Barkley Sound on the west coast of Vancouver Island, British Columbia, Canada. Young adult M. trossulus (29-35 mm shell length) collected from wave-exposed sites displayed striking differences in shell morphology from M. trossulus from sheltered sites. Mytilus trossulus from wave-exposed habitats had a lower shell height / shell width ratio and a thicker shell; they also had higher, more robust dysodont teeth, as well as a thicker hinge ligament. Examination of juveniles (12-14 mm shell length) collected from a wave-exposed shore and a sheltered shore separated by only a few hundred metres displayed similar differences in shell morphology and ligament thickness. These observations suggest that wave exposure is the cause of the observed differences in shell morphology and ligament thickness in M. trossulus. This is the first report of a correlation between wave exposure and morphology of the dysodont teeth and ligament in a bivalve. We discuss potential selective pressures that may favor characteristics of wave-exposed shells, including the probability of dislodgment and misalignment of valves in high wave energy areas.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.572
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.013
GPT teacher head0.196
Teacher spread0.183 · 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

Citations70
Published2000
Admission routes2
Has abstractyes

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