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Record W1988153935 · doi:10.1080/01431161.2013.804222

Regional algorithms for remote-sensing estimates of total suspended matter in the Beaufort Sea

2013· article· en· W1988153935 on OpenAlexaffabout
Shilin Tang, Pierre Larouche, Andrea Niemi, Christine Michel

Bibliographic record

VenueInternational Journal of Remote Sensing · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsFisheries and Oceans Canada
FundersNorth Dakota State University
KeywordsBeaufort seaOceanographyChinaChinese academy of sciencesBeaufort scaleFisheryGeographyMarine fisheriesEnvironmental scienceFish <Actinopterygii>GeologyArchaeologySea iceBiology

Abstract

fetched live from OpenAlex

Shilin Tangab, Pierre Larouchec, Andrea Niemia & Christine Michela* a Fisheries and Oceans Canada, Freshwater Institute , Winnipeg , R3T 2N6 , Canada b State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences , Guangzhou , 510301 , China c Fisheries and Oceans Canada, Institut Maurice-Lamontagne, Mont-Joli , Québec , Canada , G5H 3Z4 * E-mail: Christine.Michel@dfo-mpo.gc.ca. The large and variable riverine inflow to Arctic continental shelves strongly influences their chemical, biological, and optical properties. The Beaufort Sea receives the largest amount of suspended sediments amongst all Arctic shelves, with sediment-laden Mackenzie river waters strongly influencing bio-optical properties on the shelf. Here, we developed two regional algorithms for the estimation of total suspended matter (TSM) concentration using Medium Resolution Imaging Spectrometer (MERIS) spectral bands, based on in situ optical and suspended particulate data collected in the summer during the Canadian Arctic Shelf Exchange Study (CASES) in 2004 and during the Arctic Coastal Ecosystem Study (ACES) in 2010. The band ratio (where R rs is remote-sensing reflectance) R rs,560/R rs,490 was best correlated with low TSM concentrations (less than 3.0 g m−3), while higher TSM concentrations were well correlated to R rs,681/R rs,560. An empirical piecewise algorithm is thus proposed with the switch between the ratios being triggered by R rs,681/R rs,560 at a threshold value of 0.6. The second algorithm made use of support vector machines (SVMs) as a nonlinear transfer function between TSM concentrations and remote-sensing reflectance ratios R rs,681/R rs,560, R rs,665/R rs,560, and R rs,560/R rs,490. Results show that both algorithms perform better (31% and 25%, respectively) than other published TSM algorithms including the MERIS Case 2 water processor (C2R) neural network algorithm in the study area.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.252
Teacher spread0.231 · 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 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

Citations25
Published2013
Admission routes2
Has abstractyes

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