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Record W2606085896 · doi:10.3897/bdj.5.e12409

DNA barcoding the fishes of Lizard Island (Great Barrier Reef)

2017· article· en· W2606085896 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiodiversity Data Journal · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsUniversity of GuelphBell (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityTotal FoundationCommonwealth Scientific and Industrial Research OrganisationGenome Canada
KeywordsDNA barcodingBarcodeGreat barrier reefFisheryMarine fishBiologyReefGenusLizardFish <Actinopterygii>EcologyGeographyZoologyComputer science

Abstract

fetched live from OpenAlex

To date the global initiative to barcode all fishes, FISH-BOL, has delivered barcodes for approximately 14,400 of the 30,000 fish species; there is still much to do to attain its ultimate goal of barcoding all the world’s fishes. One strategy to overcome local gaps is to initiate short but intensive efforts to collect and barcode as many species as possible from a small region – a barcode ‘blitz’. This study highlights one such event, for the marine waters around Lizard island in the Great Barrier Reef (Queensland, Australia). Barcode records were obtained from 983 fishes collected over a two-week period. The resulting dataset comprised 358 named species and another 13 species that presently can only be reliably identified to genus level. Overall, this short expedition provided DNA barcodes for 13% of all marine fish species known to occur in Queensland.

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.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.087
GPT teacher head0.298
Teacher spread0.211 · 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