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Record W1560825394 · doi:10.3989/scimar.2005.69s155

Physical forcing and phytoplankton distributions

2005· article· en· W1560825394 on OpenAlexafffund
Trevor Platt, Heather A. Bouman, Emmanuel Devred, César Fuentes‐Yaco, Shubha Sathyendranath

Bibliographic record

VenueScientia Marina · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie UniversityBedford Institute of Oceanography
FundersCanadian Space Agency
KeywordsPhytoplanktonForcing (mathematics)Environmental scienceOceanographyClimatologyGeologyBiologyEcologyNutrient

Abstract

fetched live from OpenAlex

At the global and regional scales, the distribution and abundance of marine phytoplankton are under the control of physical forcing. Moreover, the community structure and the size structure of phytoplankton assemblages also appear to be under physical control. Areas of the ocean with common physical forcing (ecological provinces) may be expected to have phytoplankton communities that respond in a similar fashion to changes in local forcing, and with ecophysiological rate parameters that are predictable from local environmental conditions. In modelling the marine ecosystem, relevant parameters may be assigned according to a partition into ecological provinces. To the extent that physical forcing of the ocean is not constant within or between years, the boundaries of the provinces should be considered as dynamic. The dynamics and the associated changes in taxa can be revealed by remote sensing.

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)0.003
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 score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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

Citations79
Published2005
Admission routes2
Has abstractyes

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