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Record W2025208994 · doi:10.1029/2000jc000597

Bio‐optical properties of the Labrador Sea

2003· article· en· W2025208994 on OpenAlexaffabout
Glenn F. Cota, W. G. Harrison, Trevor Platt, Shubha Sathyendranath, Venetia Stuart

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie UniversityBedford Institute of Oceanography
Fundersnot available
KeywordsPhytoplanktonDetritusChlorophyll aLatitudeEnvironmental scienceAbsorption (acoustics)OceanographyParticulatesChlorophyllBiomass (ecology)Atmospheric sciencesBackscatter (email)AlgaeNutrientBiologyGeologyBotanyEcologyPhysicsOptics

Abstract

fetched live from OpenAlex

Three cruises were conducted during fall and spring in the Labrador Sea to investigate the effects of bio‐optical properties on satellite retrievals of phytoplankton chlorophyll in this important high‐latitude ecosystem. Taxon‐specific and regional differences were found. Diatoms had ∼1.5 lower chlorophyll‐specific absorption but significantly higher reflectance ratios than prymnesiophytes. Particulate absorption at 443 nm for total, phytoplankton, and “detrital” fractions was related to chlorophyll, but values were lower than reported for lower latitudes. Decreased particulate absorption is attributed primarily to pigment packaging, while low backscattering to scattering ratios result from a lower relative abundance of bacteria and picophytoplankton with more large phytoplankton. Soluble absorption was not related to chlorophyll. A four‐component model with low, variable backscatter fractions and the observed absorption coefficients for phytoplankton, “detritus,” and soluble materials reproduces the measured reflectance spectra. Global chlorophyll algorithms tend to underestimate biomass at high latitudes, whereas regionally tuned algorithms provide more reliable retrievals. Taxon‐specific algorithms show promise, but given limited ranges, small sample sizes, and overlapping reflectance ratios they remain premature.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.258
Teacher spread0.224 · 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

Citations90
Published2003
Admission routes2
Has abstractyes

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