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Record W7101624164 · doi:10.21966/vdyq-r660

Environmental DNA survey of Calvert Island, British Columbia, 2021

2021· dataset· W7101624164 on OpenAlexaffabout

Bibliographic record

VenueHakai Institute · 2021
Typedataset
Language
FieldComputer Science
TopicCybersecurity and Information Systems
Canadian institutionsFisheries and Oceans CanadaMcGill University
Fundersnot available
KeywordsEnvironmental DNABiodiversityHabitatInvertebrateFish <Actinopterygii>Marine habitatsBenthic habitatFishingDNA sequencing

Abstract

fetched live from OpenAlex

This data package contains molecular resources derived from a large DNA-based survey of marine biodiversity carried out around Calvert Island, British Columbia, in 2021. During this survey we collected triplicate seawater samples from 208 sites across a marine region roughly 100km2 in scale, focussed on nearshore habitats of kelp, seagrass, and rocky reefs. All samples were collected adjacent to the substrate using a niskin bottle, filtered using 0.22μm sterivex filters, and the resulting environmental DNA was used for amplicon sequencing to infer the communities of fish (12S rRNA gene) and invertebrates (COI) at each location. These data are part of a larger collaboration between researchers from McGill University, Fisheries and Oceans Canada, and the Hakai Institute, with the goal of optimizing the use of environmental DNA for monitoring Canada’s network of Marine Protected Areas.

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: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.012
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.013

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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
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
Published2021
Admission routes2
Has abstractyes

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