Ocean Colour remote sensing in the Southern Laptev Sea: evaluation and applications
Bibliographic record
Abstract
Abstract. Enhanced permafrost warming and increased arctic river discharges have heightened concern about the input of terrigeneous matter into Arctic coastal waters. We used optical operational satellite data from the Ocean Colour sensor MERIS onboard the ENVISAT satellite mission for synoptic monitoring of the pathways of terrigeneous matter in the southern Laptev Sea. MERIS satellite data from 2006 on to 2011 were processed using the Case2Regional Processor, C2R, installed in the open-source software ESA BEAM-VISAT. Since optical remote sensing using Ocean Colour satellite data has seen little application in Siberian Arctic coastal and shelf waters, we assess the applicability of the calculated MERIS parameters with surface water sampling data from the Russian-German ship expeditions LENA2010 and TRANSDRIFT-XVII taking place in August and September 2010 in the southern Laptev Sea. The surface waters of the southern Laptev Sea are characterized by low transparencies, due to turbid river water input, terrestrial input by coastal erosion, resuspension events and, therefore, high background concentrations of Suspended Particulate Matter, SPM, and coloured Dissolved Organic Matter, cDOM. The mapped calculated optical water parameters, such as the first attenuation depth, Z90, the attenuation coefficient, k, and Suspended Particulate Matter, SPM, visualize resuspension events that occur in shallow coastal and shelf waters indicating vertical mixing events. The mapped optical water parameters also visualize that the hydrography of the Laptev Sea is dominated by frontal meanders with amplitudes up to 30 km and eddies and filaments with diameters up to 100 km that prevail throughout the ice-free season. The meander crests, filaments and eddy-like structures that become visible through the mapped MERIS C2R parameters indicate enhanced vertical and horizontal transport energy for the transport of terrigenous and living biological matter in the surface waters during the ice-free season.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".