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Record W4251538253 · doi:10.7287/peerj.preprints.26495

Improving monitoring by understanding the patterns and drivers of biodiversity on Canada’s Glass Sponge Reefs

2018· preprint· en· W4251538253 on OpenAlexaffabout
Stephanie K. Archer, Lily Burke, Anya Dunham

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsReefBiodiversitySpongeFisheryCoral reefOceanographyFishingFringing reefHabitatGeographyEcologyGeologyPaleontologyBiology

Abstract

fetched live from OpenAlex

Glass sponge reefs, built by up to three species of dictyonine hexactinellid sponges, are hotspots of biodiversity that are unique to the waters of the Pacific continental shelf. Since 2012 we have surveyed the biological community on 21 sponge reefs from the Strait of Georgia to Chatham Sound, British Columbia. Here we present patterns of biodiversity found on glass sponge reefs and associations between common reef-dwelling organisms and sponge reef habitat categories: no visible reef, dead reef, mixed reef, live reef, and dense live reef. Further we share our findings regarding energy flow through the reef community and the implications for the maintenance of biodiversity in this system. We discuss how our findings inform monitoring in the new Hecate Strait and Queen Charlotte Sound Glass Sponge Reefs Marine Protected Areas and the many other conservation-based fishing closures centered on sponge reefs. Finally, we show how this research has led to the development of novel monitoring methods, namely the application of passive acoustic monitoring on the sponge reef ecosystem.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.194
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes2
Has abstractyes

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