Modifying the Jackson index to quantify the relationship between geology, landscape structure, and water transit time in steep wet headwaters
Bibliographic record
Abstract
Abstract The relationship between stream water mean transit time (MTT), catchment geology, and landscape structure is still poorly characterized. Here, we present a new simple index that builds on the Jackson, Bitew, and Du (2014) index that focuses specifically on permeability contrasts at the soil–bedrock interface and digital elevation model‐based physical flow path measurements to identify broad landscape trends of moisture redistribution in the subsurface of steep wet headwater catchments. We use this index to explore the relationship between geology, landscape structure, and water transit time through the lens of landscape anisotropy. We hypothesize that catchments with a greater tendency to shed water laterally will correlate with younger stream water MTT and catchments with a greater tendency to infiltrate water vertically will correlate with older stream water MTT. We tested the new index at eight geologically diverse Pacific Rim catchments in Oregon, Japan, and New Zealand. The new index explained 77% of the variability in measured stream water MTT across these varied sites. These findings suggest that critical zone anisotropy and catchment form are first‐order controls on the time scales over which catchments store and release their water and that a simple index may usefully capture this relationship.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".