Digital landscapes of deglaciation: identifying Late Quaternary glacial lake outburst floods using LiDAR
Bibliographic record
Abstract
Abstract High resolution DEMs obtained from LiDAR topographic data have led to improved landform inventories (e.g. landslides and fault scarps) and understanding of geomorphic event frequency. Here we use airborne LiDAR mapping to investigate meltwater pathways associated with the Tweed Valley palaeo ice‐stream (UK). In particular we focus on a gorge downstream of Palaeolake Milfield, previously mapped as a sub‐glacial meltwater channel, where the identification of abandoned headcut channels, run‐up bars, rock‐cut terrace surfaces and eddy flow features attest to formation by a sub‐aerial glacial lake outburst flood (GLOF) caused by breaching of a sediment dam, likely an esker ridge. Mapping of these landforms combined with analysis of the gorge rim elevations and cross‐section variability revealed a two phase event with another breach site downstream following flow blockage by higher elevation drumlin topography. We estimate the magnitude of peak flow to be 1–3 × 103 m3/s, duration of the event to range from 16–155 days, and a specific sediment yield of 107–109 m3/km2/yr. We identified other outburst pathways in the lower Tweed basin that help delineate an ice margin position of the retreating Tweed Valley ice stream. The results suggest that low magnitude outburst floods are under‐represented in Quaternary geomorphological maps. We therefore recommend regional LiDAR mapping of meltwater pathways to identify other GLOFs in order to better quantify the pattern of freshwater and sediment fluxes from melting ice sheets to oceans. Despite the relatively low magnitude of the Till outburst event, it had a significant impact on the landscape development of the lower Tweed Valley through the creation of a new tributary pathway and triggering of rapid knickpoint retreat encouraging new regional models of post‐glacial fluvial landscape response. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 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) | 0.001 | 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".