Multivariate analysis as a tool to infer hydrologic response types and controlling variables in a humid temperate catchment
Bibliographic record
Abstract
Abstract We assess the ability of multivariate statistical analyses applied to event hydrographs parameters, to characterize a catchment hydrological behaviour. Motivation for such an approach lies in the fact that streamflow records have yet to be exploited to their full potential towards hydrological interpretation and can be used to infer a catchment state of connectivity from a qualitative standpoint. We have therefore processed 96 event hydrographs from a small headwater temperate humid forested catchment using principal component analysis, variation partitioning and classification tree analysis. These techniques prove to be promising in discriminating contrasted types of hydrologic responses (e.g. low‐ vs high‐magnitude events, slow vs quick timing events), identifying the main hydro‐meteorological variables that control these responses and determining thresholds values of the hydro‐meteorological variables leading to a switch between catchment response types. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".