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Record W2028709682 · doi:10.1364/ao.54.000546

Hyperspectral absorption coefficient of “pure” seawater in the range of 350–550  nm inverted from remote sensing reflectance

2015· article· en· W2028709682 on OpenAlexafffund
Zhongping Lee, Jianwei Wei, Ken Voss, Marlon R. Lewis, Annick Bricaud, Yannick Huot

Bibliographic record

VenueApplied Optics · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversité de SherbrookeDalhousie University
FundersInstitut national des sciences de l'UniversNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationCentre National de la Recherche ScientifiqueCentre National d’Etudes SpatialesEuropean Space AgencyNational Aeronautics and Space Administration
KeywordsHyperspectral imagingSeawaterRemote sensingOcean colorAbsorption (acoustics)Attenuation coefficientMaterials scienceOpticsReflectivityMolar absorptivityRange (aeronautics)Environmental scienceAbsorption spectroscopyGeologyPhysicsOceanographySatellite

Abstract

fetched live from OpenAlex

Hyperspectral (every 5 nm) absorption coefficients of “pure” seawater in the range of 350–550 nm are derived from remote sensing reflectance measured in oligotrophic oceans. The absorption spectrum is reduced by ∼50–70% for the 350–400-nm range and ∼5–10% for the 510–530-nm range compared with the commonly adopted standard for ocean color processing and shows different spectral curvatures. The application of this new spectrum resulted in better retrievals of the phytoplankton absorption coefficient in oligotrophic oceans and will provide better closure of remote sensing reflectance for the UV–visible domain.

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)0.000
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.216
Teacher spread0.190 · 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

Citations139
Published2015
Admission routes2
Has abstractyes

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