Overview of Ice Jams in Three Major US Rivers
Bibliographic record
Abstract
We investigated ice jams on the Yukon, the Platte, and the Connecticut Rivers. Information on the dates of jam occurrence and the latitude and longitude of each jam was found in the Ice Jam Database (www.crrel.usace.army.mil/icejams/). Information was available for 255 ice jams on the Platte River, 210 ice jams on the Yukon River; and 94 ice jams on the Connecticut River. Each ice jam was associated with the closest NWS meteorological station and the closest downstream USGS gaging station for which there were consistent records. The daily discharge and the accumulated freezing degree days (AFDD) associated with each ice jam event along with the changes in discharge and AFDD in the time period immediately prior to each event were determined. As the majority of entries in the database have been entered with "Unknown " type, the type of jam was determined independently for each ice event based on the flow conditions immediately prior to the jam formation and the time of year that the jam occurred. Ice jams were classified as freezeup jams if the river discharge decreased prior to the ice jam formation, as indicated by the change in discharge over the previous 5 days. Any jam not classified as a freezeup jam was classified as a breakup jam. Surveys of the day-of-year and discharge for each ice jam location along the entire length of each river are presented. Information on the day-of-year was used to classify the jam formation as progressive (Yukon), semi-progressive (Platte) or non-progressive (Connecticut). Histograms of freezeup and breakup jams by the day-of-year and discharge show that the classification of jam type by discharge produces consistent and rational results
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".