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Record W2244318781

Seasonal inertia of macrozooplankton communities in the Lazarev Sea

2008· article· en· W2244318781 on OpenAlexaff
Brian P. V. Hunt, Evgeny A. Pakhomov, Volker Siegel, Ulrich Bathmann

Bibliographic record

VenueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut) · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPelagic zoneOceanographySeasonalityPopulationEnvironmental scienceGeographyBiologyEcologyGeologyDemography
DOInot available

Abstract

fetched live from OpenAlex

Lazarev Sea macrozooplankton communities were sampled by RMT8 in the upper 200 m of the water column in summer (December-January), autumn (April-May) and winter (July-August) during the German SO-GLOBEC (2004-2007). Diphyes antarctica, chaetoganths and euphausiids were the major contributors to total densities, each averaging > 10 ind.1000m-3 in all seasons. Community Bray-Curtis similarities were about 68% within seasons and only slightly less between seasons (about 61%). The major contributors to seasonal dissimilarity were Thysanoessa macrura (14 ind.1000m-3) and Limacina helicina (o.7 ind.1000m-3) occuring predominantly in summer, Euphausia superba occurring predominantly in autumn and winter (>15 ind.1000m-3), Eukrohnia hamata decreasing from about 7 to 1 ind.1000m-3 between summer and winter, and the winter peak of Ihlea racovitzai (2 ind.1000m-3). Changes were primarily attributed to migration out of the epipelagic layer and seasonal population decline. Overall, the similarity between seasons was high and total densities did not differ significantly between seasons, averaging 52.47, 59.14 and 46.28 ind.1000m-3 in summer, autumn and winter respectively. In view of low winter primary production, it is predicted that the epipelagic had changed from bottom-up controlled in summer to top-down controlled in winter.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.230
Teacher spread0.208 · 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.

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
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueHelmholtz-Zentrum für Polar-und Meeresforschung (Alfred-Wegener-Institut)Same topicMarine and environmental studiesFrench-language works237,207