Long-Term High Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters retrieved using 30-year Landsat Archive Imagery
Bibliographic record
Abstract
The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts ecosystems and the subsistence economy of the local population. Yet, the patterns of sediment dispersal in nearshore zones are not well known, because ships do not often reach shallow waters and satellite remote sensing is traditionally focused on less dynamic environments. We use the extensive Landsat archive to investigate sediment dispersal patterns specifically on Arctic nearshore environments, where field measurements are scarce. Multiple Landsat scenes were combined to calculate means and medians of sediment dispersal and sea surface temperatures under changing seasonal wind conditions in the nearshore zone of Herschel Island Qikiqtaruk in the western Canadian Arctic since 1982. We use the Landsat red and thermal wavebands, as well as a recently published water turbidity algorithm to relate archive wind data to suspended sediment concentrations and sea surface temperature. We also map the transport pathways of water and sediment along the coast at high spatial resolution. Our results show that these pathways are clearly related to the prevailing wind conditions, being ESE and NW. During easterly wind conditions, the Mackenzie River plume expands over extensive parts of the western Canadian Beaufort Shelf and is the main explanatory variable for sediment dispersal and sea surface temperature distributions. During northwesterly wind conditions, the influence of the Mackenzie River Plume is negligible and regional currents along the coast are the main driver. Our results highlight potential of high spatial resolution Landsat imagery to detect small scale hydrodynamic features, but also show the need to specifically tune a bio-optical model for Arctic nearshore environments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".