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Record W2138907908 · doi:10.2307/1543217

How To Tell a Sea Monster: Molecular Discrimination of Large Marine Animals of the North Atlantic

2002· article· en· W2138907908 on OpenAlex
Steven M. Carr, H. Dawn Marshall, Kimberley A. Johnstone, L. M. Pynn, Garry B. Stenson

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBiological Bulletin · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsFisheries and Oceans CanadaMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsBiologySperm whaleBayPolymerase chain reactionGenBankZoologyCreaturesMitochondrial DNADNA barcodingEvolutionary biologyTaxonFisheryGeneticsEcologyGenePaleontologyOceanographyBiochemistry

Abstract

fetched live from OpenAlex

Remains of large marine animals that wash onshore can be difficult to identify due to decomposition and loss of external body parts, and in consequence may be dubbed "sea monsters." DNA that survives in such carcasses can provide a basis of identification. One such creature washed ashore at St. Bernard's, Fortune Bay, Newfoundland, in August 2001. DNA was extracted from the carcass and enzymatically amplified by the polymerase chain reaction (PCR): the mitochondrial NADH2 DNA sequence was identified as that of a sperm whale (Physeter catodon). Amplification and sequencing of cryptozoological DNA with "universal" PCR primers with broad specificity to vertebrate taxa and comparison with species in the GenBank taxonomic database is an effective means of discriminating otherwise unidentifiable large marine creatures.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.263

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.240
Teacher spread0.213 · 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