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Record W4285733237 · doi:10.3390/jmse10070971

A New Method of Determining Glass Sponge Reef Adaptive Management Zones for the Hecate Strait and Queen Charlotte Sound Marine Protected Area

2022· article· en· W4285733237 on OpenAlexafffund
Guoqi Han, Jon Chamberlain, Colin Webber, Charles G. Hannah

Bibliographic record

VenueJournal of Marine Science and Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsFisheries and Oceans Canada
FundersFisheries and Oceans CanadaCanadian Space Agency
KeywordsReefOceanographyGeologyDredgingFishingSeabedSedimentEnvironmental scienceFisheryBeach nourishmentShoreHydrology (agriculture)Paleontology

Abstract

fetched live from OpenAlex

The world’s largest living glass sponge reefs, located in the Hecate Strait and Queen Charlotte Sound off British Columbia, are impacted by bottom contact fishing gear. The existing Adaptive Management Zones (AMZs) for the protection of these reefs were determined by considering the potential exposure of glass sponges to suspended sediment due to mobile bottom-contact fishing, but without considering their pumping arrest threshold concentrations. Here, we develop a new method that uses a sediment transport model under horizontally variable near-bottom currents and newly available sponge reef pumping arrest thresholds to determine the size and shape of AMZ for the northern reefs in the Hecate Strait and Queen Charlotte Sound Marine Protected Area. The resulting AMZ is larger than the existing AMZ due to the observation that the largest currents are not always in the direction of the dominant tidal flows, the introduction of the new pumping arrest threshold, and the inclusion of a background sediment concentration. The new AMZ boundary could provide more adequate protection for the glass sponge reefs from the effects of sedimentation induced by mobile, bottom-contact fishing activity. The new method is applicable to other glass sponge reefs in British Columbia waters.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.320

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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 designOther design
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

Citations3
Published2022
Admission routes2
Has abstractyes

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