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Taxonomic composition and abundance of planktonic algae in the West Antarctica waters (February-March 2020)

2022· article· en· W4312395004 on OpenAlexfundno aff
Polina G. Belyaeva, Dmitry Y. Sharavin

Bibliographic record

VenueВестник Пермского университета Серия «Биология»=Bulletin of Perm University Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
FundersAlberta Agricultural Research Institute
KeywordsTransectPhytoplanktonOceanographyAbundance (ecology)AlgaePlanktonChaetocerosPopulationDiatomEcologyGeographyEnvironmental scienceBiologyGeologyNutrient

Abstract

fetched live from OpenAlex

Diversity, quantitative development and distribution of phytoplankton species were investigated. The study area included southeastern part of the Ross Sea (near Roosevelt Island) (station 3), transect 1 along the 156°W (Ross Sea at Cape Colbeck), near Russkaya station (transect 2) along the 138°W and the Bransfield Strait (transect 3) in February-March 2020, based on the data of 65th Russian Antarctic Expedition. 49 algae taxa from 7 divisions with a predominance of diatoms were identified in the phytoplankton. The spatial distribution of phytoplankton was characterized by heterogeneity which is associated with currents in the study areas, ice conditions, climatic and thermohaline factors. Development of diatoms is typical to the entire studied area. The dominant species (more than 10% of the algae population) were representatives of the genera Fragilariopsis, Chaetoceros, Actinocyclus, Corethron and Phaeocystis. The highest phytoplankton abundance (up to 264×103 cells/l) were obtained for transect 2 stations.

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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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