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The bivalves from the Scotia Arc islands: species richness and faunistic affinities

2005· article· en· W40501852 on OpenAlexaboutno aff
Diego G. Zelaya

Bibliographic record

VenueScientia Marina · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsnot available
FundersUniversidad de Magallanes
KeywordsAffinitiesSpecies richnessNova scotiaGeographyEcologyFisheryBiologyOceanographyGeologyArchaeology

Abstract

fetched live from OpenAlex

Species richness of the shallow-water bivalves from the Scotia Arc islands was studied on the basis of new collections and by reviewing extant information. Seventy-three species are recognised from the entire area. South Georgia, the South Orkney Islands and the South Shetland Islands were similar in species richness to the Antarctic Weddell sector. New records for 51 bivalve species are provided and the presence of 18 undescribed species is reported. The faunistic similarity of the islands of the Scotia Arc to the Magellan region and the Antarctic Weddell sector is re-examined. These islands show a high similarity to the Antarctic Weddell sector (49 to 85%) and a low similarity to the Magellan region (12 to 32%). Evidence from bivalves clearly supports the placement of the Scotia Arc islands within the Antarctic region.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.327
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.216
Teacher spread0.200 · 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

Citations42
Published2005
Admission routes1
Has abstractyes

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