Monitoring and Data Collection Along the Peace River during the 2004-2005
Bibliographic record
Abstract
covered during the winter months. The presence of an ice cover on the Peace River, depending on the time period during the ice season, can lead to flooding or other ice related issues that pose a risk to property as well as human health and safety. In order to manage this risk it is necessary to continually monitor the Peace River throughout the ice season to provide near real-time data collection and information to those affected by the Peace River ice. The collection of data for numerous types of river ice process for operational purposes provides the River Engineering Team of Alberta Environment with the ability to respond to developing river ice related issues in an effective and efficient manner. Data related to the meteorological, hydrologic, and hydraulic characteristics of the Peace River has been collected for a number of ice seasons. The 2004-2005 ice season on the Peace River was no exception. The data collected along the Peace River during freezeup through breakup allowed the River Engineering Team to operate effectively as well as document, in co-operation with partners, the ice season including a secondary consolidation at the Town of Peace River and an instance of low dissolved oxygen in the Peace River during breakup. The purpose of this paper is to report on provincial and federal sources of data that are available as well as to highlight some of the observations that may warrant further study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".