Informational analysis of the Canadian National Hydrometric program monitoring network
Bibliographic record
Abstract
An information theory-based analysis was used to evaluate the Canadian National Hydrometric Network. The information theory approach used a mutual information based metric known as information quality ratio (IQR) to evaluate the reproducible information available in the network. The analysis was based on available data from 1 January 2009 to 31 December 2018, and compared using average daily discharge and average daily stage data from all months, summer months, and winter months to calculate the IQR. This evaluation showed that 63–77% of hydrometric stations providing discharge data and 87–90% of hydrometric stations providing stage data, depending on the season, have an IQR equal to or greater than 0.4. These results indicate that the majority of hydrometric stations provide an average or higher level of information during the analysis period. Based on the information theory analysis, the existing hydrometric network has been shown to be average in the gauged regions of Canada, excluding the Great Plains region. These results suggest that if new stations were to be added, greater effort should be focused in the ungauged areas of Northern Canada or to bolster information content of the Great Plains.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".