Revisiting Hydrologic Sampling Strategies for an Accurate Assessment of Hydrologic Connectivity in Humid Temperate Systems
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Bibliographic record
Abstract
Abstract Hydrologic connectivity is crucial to our understanding of catchment dynamics, yet there is no consensus among hydrologists about what it is exactly, nor is there any unambiguous way of assessing it from field observations. This review is articulated around the questions: (i) what are the main variables to investigate? and (ii) what is the optimal sampling strategy for hydrologic connectivity prediction in humid temperate systems? Because there are multiple definitions for the concept of connectivity, we first identify the major variables to monitor. Then, the ability of several sampling schemes to meet specific criteria is assessed. None of the schemes fully complies with the criteria even if they are combined in a strategic way, and their individual performance is highly dependent on data resolution. While a two‐stage sampling is recommended, it reflects the difficulty in depicting the complex spatial patterns in hydrologic connectivity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it