Assessing nearshore sediment and sea surface temperature dynamics using Landsat satellite imagery at Herschel Island, western Canadian Arctic
Bibliographic record
Abstract
The Arctic is subject to substantial changes due to the greenhouse gas induced climate change. While impacts on lateral transport pathways such as rivers have been extensively studied yet, there is little knowledge about ecological and geological reactions of nearshore environments, even though those are of high importance for native communities. In this study, we use the extensive Landsat archive with comparable data from 1982 on to investigate sediment dispersal and sea surface temperatures under changing seasonal wind conditions in the nearshore zone of Herschel Island in the western Canadian Arctic. Even in the absence of an extensive in-situ dataset, we reveal clear differences between the two prevailing wind conditions (E and NW). During E wind conditions, the Mackenzie River Plume gets distributed over large parts of the Canadian Beaufort Shelf and is the main influencing factor for nearshore sediment dispersal and sea surface temperature dynamics. Contrary, the nearshore dynamics during NW wind conditions are not affected by the Mackenzie River plume, revealing the local nature of the nearshore environment. First field measurements from summer 2017 indicate that recently published SPM and turbidity models are not able to reflect this local nature and strongly underestimate reality. In future, we plan to collect an extensive validation dataset in Arctic nearshore environments to calculate accurate bio-optical models.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".