Bibliographic record
Abstract
Water temperature is a key physical habitat determinant in lotic ecosystems as it\ninfluences many physical, chemical and biological properties of rivers. Hence, a good\nunderstanding of the thermal regime of rivers is essential for effective management of\nwater and fisheries resources. This study deals with the modeling of hourly stream watertemperature using a deterministic model, an equilibrium temperature model and an artificial neural network model. The water temperature models were applied on two\nthermally different streams, namely, the Little Southwest Miramichi River (LSWM) and\nCatamaran Brook (Cat Bk) in New Brunswick, Canada.\nThe deterministic model calculated the different heat fluxes at the water surface and from\nthe streambed, using different hydrometeorological conditions. Results showed that\nmicroclimate data are essential in making accurate estimates of the surface heat fluxes.\nResults also showed that for larger river systems, the surface heat fluxes were generally\nthe dominant component of the heat budget with a correspondingly smaller contribution\nfrom the streambed (90%). As watercourses became smaller and as groundwater\ncontribution became more significant, the streambed contribution became important\n(20%).\nThe equilibrium temperature model is a simplified version of the deterministic model\nwhere the total heat flux at the surface is assumed to be proportional to the difference\nbetween the water temperature and the equilibrium temperature. The poor model\nperformance compared to the other models developed in this study suggested that the air and equilibrium temperature did not reflect entirely the total heat flux at an hourly scale.\nThe model’s best performance was in autumn, where the low water level permitted a\nmore efficient thermal exchange, whereas the presence of snowmelt conditions in spring resulted in poorer performance.\nAn artificial neural network (ANN) was also developed to predict hourly river water\ntemperatures using minimal and accessible input data. The results showed that ANN\nmodels are effective modeling tools, with similar or better results to comparable modeling studies. The ANN model performed best in summer and autumn and had poorer, but still good, performance in spring, explained by the high water levels.
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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 | 0.000 |
| 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) | 0.001 | 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".