Thaw slump activity measured using stationary cameras in time-lapse and Structure-from-Motion photogrammetry
Bibliographic record
Abstract
Thaw slumps are one of the most dynamic features in permafrost terrain. Improved temporal and spatial resolution monitoring of slump activity is required to better characterize their dynamics over the thaw season. We assess how a ground-based stationary camera array in a time-lapse configuration can be integrated with unmanned aerial vehicle (UAV)-based surveys and Structure-from-Motion processing to monitor the activity of thaw slumps at high temporal and spatial resolutions. We successfully constructed point-clouds and digital surface models of the headwall area of a thaw slump at 6- to 13-day intervals over the summer, significantly improving the decadal to annual temporal resolution of previous studies. The successfully modeled headwall portion of the slump revealed that headwall retreat rates were significantly correlated with mean daily air temperature, thawing degree-days, and average net short-wave radiation and suggest a two-phased slump activity. The main challenges were related to strong JPEG image compression, drifting camera clocks, and highly dynamic nature of the feature. Combined with annual UAV-based surveys, the proposed methodology can address temporal gaps in our understanding of factors driving thaw slump activity. Such insight could help predict how slumps could modify their behavior under changing climate.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".