Surface ice observations on the St. Lawrence River using infrared thermography
Bibliographic record
Abstract
Abstract Winter navigation requires regular and informative ice observations in order to provide safe and effective waterways. Acquiring field data is a costly, risky and difficult operation that can be greatly aided by airborne remote sensing. This paper reports on a field campaign designed to assess the utility of infrared thermography as a means of river ice monitoring. For this study, data were acquired on the St. Lawrence River. In March 2008, airborne infrared georeferenced images were acquired between Montreal and Quebec City, Canada. Following appropriate corrections and calibration, each image provided direct information on surface ice characteristics including superficial concentration and temperature. Ice floe thickness could also be deduced through the numerical analysis of ice surface temperature and near surface air temperature. Taken as a whole, the set of images reveals phenomena and patterns in the observed ice floes. These include: surface median temperature (from −2 to 0°C); median thickness (from 1 to 3 cm); concentration (highly variable along successive reaches, varying from 0 to 100%); shape of floes (typically more oblong than circular); mean floe area (up to 5 ha) and number of floes per hectare (typically from 5 to 50). Along successive reaches, sectorial trends are observed for differing hydraulic conditions. Taken together, theses statistics bring out the variability inherent in the ice floes developed along the river, and lead to the conclusion that infrared thermography is a unique and effective tool for river ice monitoring, as it provides both rich visual and quantitative information. Copyright © 2010 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".