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Record W2074740131 · doi:10.1155/2010/410129

Long-Term Relationships between the Marine Environment, Krill and Salps in the Southern Ocean

2010· article· en· W2074740131 on OpenAlexaff
Chung Il Lee, Evgeny A. Pakhomov, Angus Atkinson, Volker Siegel

Bibliographic record

VenueJournal of Marine Biology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of British Columbia
FundersDivision of Ocean SciencesMinistry of Education, Science and TechnologyNational Research Foundation of KoreaNational Research Foundation
KeywordsAntarctic krillKrillEuphausiaOceanographySalinityPelagic zoneWater columnAbiotic componentEnvironmental scienceBiologyEcologyGeology

Abstract

fetched live from OpenAlex

Long-term variations (1975–2002) in climatology of marine environmental parameters, Antarctic krill, Euphausia superba , and the pelagic tunicate, Salpa thompsoni , were compared within the Atlantic Sector of the Southern Ocean. Sea water temperature in the top 400 m increased at a rate of 0.020–0.030 ° C ⋅ yr −1 , which was accompanied by the dissolved oxygen decline. Top 100 m water layer became fresher with lower concentrations of phosphates and nitrates, while at subsurface layers (200–400 m) both salinity and nutrients showed small increasing trend. Unlike phosphates and nitrates, silicate concentrations decreased in the entire water column. Shorter-term water temperature dynamics closely correlated with the El Nino events expressed as the Southern Oscillation Index which in turn was linked to the propagation of the Antarctic Circumpolar Wave (ACW). The variations of sea-ice extent matched well the changes in both air and water temperatures. In general, abundance of krill and salps showed opposite to each other trends. Due to large area considered in this study, no significant relationships between abiotic factors and both krill and salps were found. However, our analysis demonstrated that krill abundance was greater in years with lower sea water temperature, greater sea-ice extent and higher nutrient concentration, while salps showed the opposite pattern.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.015
GPT teacher head0.209
Teacher spread0.195 · 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 teacher head, 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

Citations35
Published2010
Admission routes1
Has abstractyes

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