Bibliographic record
Abstract
A 9 year field program was undertaken from 19911992 to 19992000 to (i) measure the loads in the ice sheet near a dam, (ii) measure the load distribution between a gate and a pier, and (iii) compare the loads on wooden and steel stoplogs. Parallel work was conducted to develop analytical predictors for ice loads. Much progress has been made. One of the most significant findings has been to identify the importance of water level changes on the resulting ice loads. Ice loads are much higher and more variable (compared to purely thermal loads) when significant, but not excessive, water level changes occur. Methods have been developed to predict the ice load. The algorithms predict thermal loads well. They are less accurate for loads produced by a combination of water level and ice temperature changes. An environmental model was developed, and the predictions using the model compare well with the measured data. Hindcast analyses were carried out to evaluate the distribution of expected ice temperature changes and thermal events. With respect to the loads on gates and stoplogs, an analytical method was developed to extend the results obtained in this project to other stoplog or gate configurations (i.e., spans, flexural rigidities, etc.) and pier lengths.Key words: ice loads, dam(s).
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".