MétaCan
Menu
Back to cohort
Record W2942881931 · doi:10.1016/j.jglr.2019.04.001

The Late Pleistocene mollusk fauna of Selitrennoye (Astrakhan province, Russia): A natural baseline for endemic Caspian Sea faunas

2019· article· en· W2942881931 on OpenAlexvenueno aff
Sabrina van de Velde, Т.А. Yanina, Thomas A. Neubauer, Frank P. Wesselingh

Bibliographic record

VenueJournal of Great Lakes Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsNaturalis Biodiversity CenterEuropean Commission
KeywordsFaunaBiodiversityEcologyPleistoceneHabitatAbundance (ecology)Baseline (sea)EndemismEarly PleistoceneGeographyOceanographyBiologyPaleontologyGeology

Abstract

fetched live from OpenAlex

The native Caspian Sea fauna underwent severe changes since the early 20th century, mostly due to anthropogenic activities. However, the nature, magnitude and rate of biodiversity change can only be assessed by comparison with natural baseline settings. A mostly in-situ mollusk fauna retrieved from Late Pleistocene (Hyrcanian, 107 ± 7 ka) deposits at Selitrennoye (Astrakhan province, Russia) provides a snapshot of a natural Caspian assemblage. In total, 24 gastropod and 13 bivalve species were identified. The fauna is predominantly endemic/native (94% in species numbers and 99% in abundance) and is dominated by three families (Cardiidae, Dreissenidae and Hydrobiidae). The Selitrennoye assemblage is a natural baseline of a pre-invasive Caspian Sea fauna living in a shallow (15–25 m), lower mesohaline (5–8 psu) open lake environment. The species-rich Selitrennoye fauna contrasts with the largely depleted and invasive-dominated fauna that currently occupies similar habitats in the Caspian Sea and underlines the magnitude of the current biodiversity crisis.

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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

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

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 head0.027
GPT teacher head0.276
Teacher spread0.249 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Great Lakes ResearchSame topicMarine and environmental studiesFrench-language works237,207