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Record W2508523587

Structural characteristics of self-excavated burrows by boring polydorid species (Polychaeta, Spionidae)

2000· article· en· W2508523587 on OpenAlexaboutno aff
Waka Sato‐Okoshi, Kenji Okoshi

Bibliographic record

VenueBulletin of Marine Science · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsnot available
Fundersnot available
KeywordsBurrowGeologyBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Burrow structure of three common boring spionids, Polydora pygidialis, Dipolydora giardi, and Dipolydora bidentata, inhabiting Barkley Sound, Vancouver Island, Canada, was examined using SEM. The surface of burrows excavated in nine mollusc shells consisting of four types of microstructure (foliated, nacreous, prismatic, and crossed lamellar) was studied to obtain information about the boring mechanism of these species. Small characteristic concentric-edged holes were observed on the surface of the burrows of each species; however, the existence of concentric-edged holes depended on the difference of the shell microstructure. Worm-eaten crystals, bottom parallel scratches, and lateral scratched structures were also observed on the surface of the burrows. We speculated that the polydorid worms (1) secrete some chemical substance which directly acts and dissolves the crystals and a part of organic matrix first and makes them weaker and (2) scratch and loosen them mechanically both in moving back and forth along the burrow and by a rotary motion within the burrow. We further speculated that some chemical substance secreted by worms and organs involved in the boring activity may be the same among the polydorid species, that the boring mechanism among polydorid species may be the same, and that the existence of concentric-edged holes or worm-eaten structure provides evidence of polydorid infestation.

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 score0.079
Threshold uncertainty score0.922

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0790.000

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations24
Published2000
Admission routes1
Has abstractyes

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