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Record W6982481740

The Influence of Sediment Characteristics on the Burrowing Behavior of Juvenile Razor Clams, Ensis directus

2015· article· en· W6982481740 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalCommons (California Polytechnic State University) · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicAncient Egypt and Archaeology
Canadian institutionsnot available
Fundersnot available
KeywordsSedimentBivalviaRange (aeronautics)MolluscaJuvenile
DOInot available

Abstract

fetched live from OpenAlex

Ensis directus, or the Atlantic razor clam, is an infaunal bivalve species whose geographic range extends along the Atlantic coast of North America, from Canada to South Carolina. In this study, I examined the burrowing behavior of large juvenile razor clams (shell length: 60-78 mm) in two sediment types: fine mud sediment and coarse sand sediment. I categorized the burrowing behavior into four independent phases: recovery, exploration, initiation, and tunneling and recorded the proportion and time of completion of each stage of burrowing. With each clam having been exposed to both sediment types, more razor clams burrowed in the fine mud sediment and did so more quickly than in the coarse sand sediment in all phases of burrowing behavior, with statistical significant differences in the exploration, initiation, and tunneling phases between the two sediment types. Measurements of the shear and compressive strengths of both sediment types determined that the coarse sand sediment is more resistant to sediment deformation. Lastly, I utilized pressure sensors to correlate the phases of burrowing activities with changes in the pressure within sediment porewater, noting that larger changes in porewater pressure occurred while burrowing in the fine mud sediment compared to the coarse sand sediment. This research determined that the burrowing behavior of this species is sediment-dependent and should be taken into account to facilitate the establishment of razor-clam aquaculture.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.452

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 studies0.0000.001
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 head0.028
GPT teacher head0.211
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

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