Comparing Discharge Computation Methods in Great Lakes Connecting Channels
Bibliographic record
Abstract
Records of discharge for the connecting channels within the Great Lakes Basin are important to national governments of Canada and the United States and the various water management agencies and users in the basin. For more than 100 years, the official discharge records for the St. Clair and Detroit Rivers, two connecting channels within the Great Lakes Basin, have been computed using various stage-fall-discharge (SFQ) methods. However, as a result of technological advancements, newer methods have recently been considered for discharge computations. In this study, three discharge computation methods were compared: two SFQ methods and the index-velocity discharge (IVQ) method. Although the two SFQ methods have significantly different assumptions and use different data from the index-velocity method, the differences between the computed discharges derived from the methods are small, especially as the time step approaches monthly discharge values. Statistical analyses of discharge measurements and discharges computed using each of these methods indicate that there is no substantive difference in the discharges computed using the three methods. However, the IVQ method provides distinct advantages over the SFQ methods, including increased temporal resolution of computed discharge (minutes versus daily) and the ability to account for changes caused by aquatic vegetation and ice. Based on the results of the comparisons described herein, the IVQ discharge computation method is the most appropriate method for discharge computation in the St. Clair and Detroit Rivers. Updated SFQ equations for the St. Clair and Detroit Rivers, also presented herein, can be used to compute discharge during periods of missing or invalid IVQ record.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".