Evaluating top-of-ice elevation using air-borne LiDAR
Bibliographic record
Abstract
Abstract An improved understanding of river ice processes in cold regions is crucial due to the impact of ice jams on water levels. Increasing water levels may lead to flooding and cause many problems for communities living near rivers. Monitoring ice processes, including surveying the top-of-ice profile of an ice jam, can be a very time-consuming task and may expose researchers to various hazards. To overcome these challenges, aerial data collection using Unpiloted Aerial Vehicles (UAVs) is a well-known alternative method. In previous studies, top-of-ice profiles have been obtained using discrete data collected by Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) survey equipment however, this research fills in the gaps of having a continuous profile for top-of-ice elevation by utilizing LiDAR technology combined with UAV on the Dauphin River in Manitoba, Canada. An 8 km top-of-ice profile was obtained for the Dauphin River and validated with a set of data collected by RTK-GNSS equipment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".