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Record W6894253122 · doi:10.5683/sp2/ovajld

Data from: Suspended sediment causes feeding current arrests in the glass sponge Aphrocallistes vastus

2019· dataset· en· W6894253122 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSpongeSedimentCurrent (fluid)Filtration (mathematics)TrawlingRidge

Abstract

fetched live from OpenAlex

Bottom-contact trawling generates large, moving clouds of suspended sediments that can alter the behaviour of organisms adjacent to trawl paths. While increased suspended sediment concentrations (SSCs) are known to cause glass sponges to arrest filtration in lab studies, the response of sponges to sediment in situ is not yet known. Here we describe arrest behaviours in response to increased SSCs recorded from the glass sponge Aphrocallistes vastus at the Fraser Ridge sponge reef in the Strait of Georgia, British Columbia, Canada. We identified 23 arrests of the sponges’ feeding current during experimental disturbances that raised SSC to between 10 and 80 mg l-1. Single arrests lasted 4.25 ± 1.3 min (± SD) and were characterized by a 2 cm s-1 reduction in feeding current lasting 0.5 - 3 min (mean 1.91 ± 0.97 min, n = 19). In comparison, coughing arrests varied in length (31 ± 22.89 min) and lasted 4 - 15 min (10.46 ± 5.26 min, n = 4). Coughing arrests showed a distinctive on/off pattern as sponge filtration returned to normal excurrent velocities, distinguishing them from single arrests. The onset of both arrest types was correlated with elevated SSCs (r = -0.83 to -0.92). Natural SSCs at the reef averaged 4.4 mg l-1 and were correlated with tidal flow (r = 0.86 and 0.89). The combined data provide evidence that suspended sediments can stop glass sponge feeding in situ even at SSCs below those known to be generated by trawling.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.200
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0070.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.003

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 head0.122
GPT teacher head0.358
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2019
Admission routes1
Has abstractyes

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