Shopping for hydrologically representative connectivity metrics in a humid temperate forested catchment
Bibliographic record
Abstract
In order for connectivity to serve as an effective diagnostic classification tool of hydrological behavior, it clearly matters (1) how it is measured and (2) whether the chosen measures are correlated not only to catchment‐scale antecedent moisture conditions but also to streamflow discharges. Previous studies have advocated that connectivity in shallow soil moisture patterns induces threshold‐like changes in runoff in temperate rangeland catchments but not in temperate humid forested catchments. We argue that in the latter environments, capturing critical spatial organization in soil moisture patterns depends on the way the chosen connectivity metric is built. We therefore tested a large selection of 2‐D and 3‐D connectivity measures in a temperate humid forested catchment (Laurentians, Canada). Computations were based on continuous soil moisture patterns collected on 16 occasions at four soil depths and then transformed into indicator patterns using either time‐variable or time‐invariant thresholds. Assessments of connectivity were variable depending on the computed metric, as just a few measures were significantly correlated with both antecedent moisture conditions and catchment discharges. In particular, topography‐based connectivity metrics reflected changes in catchment macrostate and stormflow response better than omnidirectional methods. Also, source‐to‐stream connectivity metrics were more hydrologically sensitive than metrics that did not consider the spatial linkage to the stream channel. These conclusions stress the importance of searching for the right connectivity metric for hydrologic prediction, especially in humid forested environments that exhibit much larger variability in soil hydrologic properties than temperate rangeland catchments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".