Development and application of a time-lapse photograph analysis method to investigate the link between tidewater glacier flow variations and supraglacial lake drainage events
Bibliographic record
Abstract
Abstract Marine-terminating glaciers may experience seasonal and short-term flow variations, which can impact rates of ice flux through the glacier terminus. We explore the relationship between variability in the flow of a large tidewater glacier (Belcher Glacier, Nunavut, Canada), the seasonal cycle of surface meltwater production and the rapid drainage of supraglacial lakes. We demonstrate a novel method for analyzing time-lapse photography to quantify lake area change rates (a proxy for net filling and drainage rates) and develop a typology of lake drainage styles. GPS records of ice motion reveal four flow acceleration events which can be linked to lake drainage events discovered in the time-lapse photography. These events are superimposed on a longer pattern of velocity variation that is linked to seasonal variation in surface melting. At the terminus of the glacier, the ice displacement associated with the lake drainage events constitutes ∼10% of the seasonally accelerated displacement or 0.4% of the total annual ice displacement (336 m a−1). While the immediate ice response to these individual perturbations may be small, these drainage events may enhance overall seasonal acceleration by opening and/or sustaining meltwater conduits to the glacier bed.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".