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Record W1986953729 · doi:10.5589/m09-017

Sea surface multispectral index model for estimating chlorophyll<i>a</i>concentration of productive coastal waters in Thailand

2009· article· en· W1986953729 on OpenAlexvenueno aff
Suwisa Mahasandana, Nitin Kumar Tripathi, Kiyoshi Honda

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsChlorophyll aRemote sensingMultispectral imageSatelliteCorrelation coefficientEnvironmental scienceImaging spectrometerMultispectral pattern recognitionSpectrometerWavelengthSampling (signal processing)Absorption (acoustics)ChlorophyllGeographyMathematicsPhysicsStatisticsBotanyBiologyOptics

Abstract

fetched live from OpenAlex

Chlorophyll a (Chl a) concentration in water can be estimated using remote sensing methodology. This study uses Chl a high absorption and high reflectance wavelengths to produce indices stable for Chl a monitoring. The relationships between the indices and Chl a are determined and then applied to satellite bands for model development. The sea surface spectrum was measured in situ using a portable spectrometer, and Chl a concentration was analyzed in the laboratory. Satellite images were obtained from Landsat-5 for the day of sampling. The results of a three-band index from the spectrometer composed of wavelengths 435, 488, and 692 nm indicate the most significant correlation with Chl a concentration. The exponential relation was observed with the highest correlation coefficient of r2 = 0.83. The index model was applied to Landsat-5 data, and the observed Chl a dataset was compared with that estimated from Landsat-5. This analysis gave a high correlation of r2 = 0.73.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.203
Teacher spread0.191 · 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 designSimulation or modeling
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

Citations15
Published2009
Admission routes1
Has abstractyes

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