The 1996 Lake Ha! Ha! breakout flood, Québec: Test data for geomorphic flood routing methods
Bibliographic record
Abstract
This paper describes a set of field data suitable for the testing and comparative assessment of geomorphic flood routing methods. The data pertain to a particularly severe and unusually well–documented flood event: the Lake Ha! Ha! breakout flood of July 1996 in the Saguenay Region of Québec. In this event, heavy rains combined with the incision of a new lake outlet caused a major flood, which significantly reworked the downstream valley. Published and unpublished data from multiple sources are assembled and co–registered in a common frame of reference. These data include vertical and oblique air photos, hydrological records, surface geology information, and digital terrain models of the pre– and post–flood valley topography. The spatial coverage encompasses the drained lake as well as the full length of the downstream valley. To meet the respective needs of two– and one–dimensional approaches, the topography is sampled on a Cartesian mesh as well as interpolated along evenly–spaced cross–sections. The data set described in the paper is provided in digital form in the electronic supplement to this special issue.
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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 | 0.032 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".