The coast from above: remote sensing tools for the investigation of Arctic coastal settings
Bibliographic record
Abstract
While concern about coastal erosion on arctic coasts arises due to increasing impacts on several northern communities and potential threats to oil and gas activities in the circum-arctic, means to document and assess precisely rates and causes of coastal erosion remain scarce due to the remote locations of arctic coastal settings. Remote sensing tools offer relatively cheap and efficient means to compensate for the remoteness of these areasWe present in this poster several applications of remote sensing tools which address the issue of coastal erosion on Herschel Island, Yukon Territory with sensors operating at different spatial and temporal scales. Optical high resolution imagery is used to map periglacial features and ground ice presence in the backshore zone and assess planimetric coastal retreat rates. High resolution stereo-pairs provide a mean to document the volume of sediment eroded from the coast as well as the quantities of total organic carbon released to the nearshore zone. Finally, investigations of the spectral characteristics of coastal landcovers using high to medium resolution imagery enables the reconstruction of several stages of landslide activity and the prediction of zones at risk on ice-rich coasts.We put into perspective these applications with the expected launches of new generations of high resolution satellites and satellite constellations and the subsequent changes in approach of the temporal and spatial scales of coastal erosion. In addition, we highlight the potential of these types of tools within the context of future arctic research as mean to provide a common baseline for coastal erosion studies in the circum-arctic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".