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Record W4367626837 · doi:10.1038/s41598-023-34231-7

The role of water mass advection in staging of the Southern Ocean Salpa thompsoni populations

2023· article· en· W4367626837 on OpenAlexaff
Natasha Henschke, Boris Espinasse, Charles A. Stock, Xiao Liu, Nicolas Barrier, Evgeny A. Pakhomov

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdvectionBiological dispersalAbundance (ecology)Water massOceanographyPopulationPeninsulaEnvironmental scienceEcologyPelagic zoneGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Salpa thompsoni is an important grazer in the Southern Ocean. Their abundance in the western Antarctic Peninsula is highly variable, varying by up to 5000-fold inter-annually. Here, we use a particle-tracking model to simulate the potential dispersal of salp populations from a source location in the Antarctic Circumpolar Current (ACC) to the Palmer Long Term Ecological Research (PAL LTER) study area. Tracking simulations are run from 1998 to 2015, and compared against both a stationary salp population model simulated at the PAL LTER study area and observations from the PAL LTER program. The tracking simulation was able to recreate closely the long-term trend and the higher abundances at the slope stations. The higher abundances observed at slope stations are likely due to the advection of salp populations from a source location in the ACC, highlighting the significant role of water mass circulation in the distribution and abundance of Southern Ocean salp populations.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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