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Record W2061710703 · doi:10.1155/2014/297631

Locomotion and Functional Spine Morphology of the Heart Urchin<i>Brisaster fragilis</i>, with Comparisons to<i>B. latifrons</i>

2014· article· en· W2061710703 on OpenAlexafffund
D. Walker, Jean-Marc Gagnon

Bibliographic record

VenueJournal of Marine Biology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEchinoderm biology and ecology
Canadian institutionsCanadian Museum of NatureUniversity of Ottawa
FundersUniversité de Montréal
KeywordsBiologySPINE (molecular biology)DiggingAnatomySea urchinMorphology (biology)DorsumZoologyEcologyCell biology

Abstract

fetched live from OpenAlex

The heart urchin Brisaster fragilis is an important bioturbator found in the Estuary and Gulf of St. Lawrence. Several adaptations allow it to move within fine sediments (e.g., test shape, spine morphology, and distribution), which are compared here to those of its Pacific sibling species B. latifrons . While ventral spatulate spines and dorsal and anterolateral curvilinear spines are similar between the two species, anterior spines differ significantly: sigmoid-shaped for B. fragilis and curvilinear for B. latifrons . This morphological difference, in addition to a narrower plastron for B. fragilis , suggests a different digging strategy. In situ video observations of B. fragilis show a “dig and move” strategy: anterior spines “dig” forward at the sediment while the plastron spines “move” the urchin into the newly created space. B. latifrons on the other hand uses an oblique rocking motion. This suggests that generalizations derived from one or few species displaying similar body shapes may not be possible. Factors such as sediment depth (i.e., the amount of sediment above the urchin) are likely to affect movement and force the animal to employ a different digging strategy, even within a single species. The role of spines for locomotion is further discussed, with additional reference to tubercle morphology.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.296

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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 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

Citations12
Published2014
Admission routes2
Has abstractyes

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