MétaCan
Menu
Back to cohort
Record W2227152853 · doi:10.5589/m08-022

The importance of a band at 709 nm for interpreting water-leaving spectral radiance

2008· article· en· W2227152853 on OpenAlexvenueno aff
Jim Gower, Stephanie King, Gary A. Borstad, Leslie Brown

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsRed edgeRadianceRemote sensingImaging spectrometerEnvironmental scienceSatellitePhytoplanktonSpectral bandsChlorophyll aSpectral resolutionPelagic zoneOceanographySpectrometerSpectral lineGeographyGeologyPhysicsChemistryHyperspectral imagingOpticsEcologyBiologyAstronomy

Abstract

fetched live from OpenAlex

We present examples of spectra from ocean and coastal waters observed with the optical satellite imager MERIS (Medium Resolution Imaging Spectrometer), which demonstrate the importance of a spectral band near 709 nm. For MERIS, this band has been shown to improve detection and study of a variety of intense, surface plankton blooms and was also instrumental in making the first satellite detection of pelagic Sargassum. The 709 nm band determines the maximum chlorophyll index (MCI), which can be used to detect a variety of features resulting from the absorption properties of chlorophyll a. A significant feature of the available spectral information is the "red edge" between 680 and 750 nm, characteristic of reflectance spectra of vegetation on land. The 709 nm band provides an observation close to the centre of this edge, helping to give a precise edge position. This paper discusses observations of Sargassum, of a variety of types of plankton bloom, and of blooms occurring in the high sediment concentrations characteristic of river plumes. We also discuss the extent to which the phytoplankton group of blooms can be distinguished using the MERIS data.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.981

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 head0.186
Teacher spread0.174 · 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 designOther design
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

Citations41
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Remote SensingSame topicMarine and coastal ecosystemsFrench-language works237,207