Bibliographic record
Abstract
In previous work, it was found that the best short-term forecasts of the flow of the Mistassibi River, in the province of Quebec (Canada), were obtained by making use of linear regression. Here, a theoretical justification for this fact is provided by showing that an excellent fit to the data is obtained with a certain bimodal distribution. This distribution is used for values of the river flow greater than or equal to 500 m3/s. For values between 500 and 1000 m3/s, a truncated exponential distribution is proposed, while for flow values greater than 1000 m3/s, a Gaussian distribution is appropriate. In the latter two cases, if we consider two-dimensional versions of the distributions, we find that the conditional expectation of the future flow value, given the current value x, is an affine function of x, thus justifying the use of linear regression to predict future flow values.Key words: stochastic modeling, hydrological forecasting, linear regression, goodness of fit, bivariate exponential.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".