Mosaic surface storages of a small boreal catchment
Bibliographic record
Abstract
Abstract Recent studies have suggested that the hydrologic connectivity of northern headwater catchments is likely controlled by antecedent moisture conditions and land cover patterns. A water storage model (EWS), based on water levels (WLs), specific yield (Sy) and surface elevation (SE) changes, was compared with a basic water budget of a small, boreal, patterned fen (13 ha) during the ice‐free period. Results showed that the EWS model reproduced well storage variations derived from the water budget. These results suggest that storage variations can be properly represented by the fluctuations of WLs when we consider the heterogeneous soil properties. However, storage deviations occurred at the daily scale and could be explained by a lack of information on water retention in unsaturated layers, canopy interceptions and preferential flows. Despite the significant impact of SE changes on the different peatland cover storage budgets (strings and lawns), using Sy mean values had a low impact on storage estimations. This can be explained by the large proportion of pools and high WLs throughout the fen. At the fen scale, high storage in the pools seemed to reduce the Sy difference between strings and lawns. The results of this study provide new insights about the complex hydrological behaviour of northern catchments and allow for conceiving new hydrological modelling perspectives. Copyright © 2014 John Wiley & Sons, Ltd.
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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".