Comparison of non-parametric and parametric water temperature models on the Nivelle River, France
Bibliographic record
Abstract
Water temperature is an important abiotic variable in aquatic habitat studies and may be one of the factors limiting the potential fish habitat (e.g. salmonids) in a stream. Stream water temperatures are modelled using statistical approaches with air temperature and streamflow as exogenous variables in the Nivelle River, southern France. Two different models are used to model mean weekly maximum temperature data: a non-parametric approach, the k-nearest neighbours method (k-NN) and a parametric approach, the periodic autoregressive model with exogenous variables (PARX). The k-NN is a data-driven method, which consists of finding, at each point of interest, a small number of neighbours nearest to this value, and the prediction is estimated based on these neighbours. The PARX model is an extension of commonly-used autoregressive models in which parameters are estimated for each period within the years. Different variants of air temperature and flow are used in the model development. In order to test the performance of these models, a jack-knife technique is used, whereby model goodness of fit is assessed separately for each year. The results indicate that both models give good performances, but the PARX model should be preferred, because of its good estimation of the individual weekly temperatures and its ability to explicitly predict water temperature using exogenous variables.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".