Bibliographic record
Abstract
This paper summarizes the results of the third in a series of tests that have been devised and coordinated by a sub-committee of the Committee on River Ice Processes and the Environment. This third phase is what has been called a “blind test”. It has been based on actual field data including bathymetry, river flow, and location of ice jam, but no data was provided to the modelers on ice thicknesses or water surface profiles. The modelers had to select appropriate parameters based on judgment, and run their models so as to provide the best possible reproduction of the actual ice jam profiles without foreknowledge of the field data. The field data on the ice jam profile was only revealed after the tests were completed and to permit comparison of the results with the prototype experience. The intent of this test was to provide a perspective on the performances of the models without the benefit of actual ice jam data upon which to calibrate the models. During the Phase 3 testing period, the authors also became aware of a new numerical model that is currently being developed by LaSalle Hydraulic Laboratory and Quebec Hydro and is being incorporated into the Danish Hydraulic Institute’s MIKE-11 software. LaSalle Hydraulic Laboratory offered to test this model, and at this time, the results of the Phase 1 test are available, and are briefly reported. 1.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.313 | 0.126 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".