Ecohydrological flow networks in the subsurface
Bibliographic record
Abstract
ABSTRACT Preferential flow in hillslope systems through subsurface networks developed from a range of botanical, faunal and geophysical processes have been observed and inferred for decades and may provide a large component of the bulk transport of water and solutes. However, our dominant paradigm for understanding and modelling hillslope hydrologic processes is still based on the Darcy–Richards matric flow framework, now with a set of additional methods to attempt to reproduce some of the aggregate function of the two‐phase system of network and matrix flow. We call for a community effort to design and implement a set of well planned experiments in different natural and constructed hillslopes, coupled with the development of new theory and methods to explicitly incorporate and couple the co‐evolution of subsurface flow networks as intrinsic components of hydrological, ecological and geomorphic systems. This is a major community challenge that can now benefit from new experimental infrastructure, renewal of older infrastructure and recent advances in sensor systems and computational capacity but will also require a sustained and organized interdisciplinary approach. Copyright © 2014 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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".