Bibliographic record
Abstract
Abstract This paper examines several issues related to hydrological boundaries and their border zones. In a two‐dimensional space, a boundary is a line that separates two domains possessing different hydrological properties or dominated by different hydrological processes, and a border is an area that experiences an edge effect owing to transitions or mixing of processes. Hydrological boundaries may be static, such as drainage divides, or dynamic, such as the edges of a seasonal snow cover. They may be open or closed to the transfer of matter and energy, although most boundaries tend to be perforated, permitting different rates of movement across different segments. Borders may be narrow or the edge effect can affect large areas, as happens to the sensible heat flux over a highly fragmented melting snowfield. The introduction of artificial boundaries, notably the grid patterns of remote sensing pixels, digital elevation models and land surface schemes, gives rise to problems of mismatch with the natural hydrological boundaries. Incorrect demarcation, omission and generalization of boundaries can produce errors that are hard to rectify. Serious biases are involved when point observations are used to calibrate parameters or to validate model outputs integrated over a bounded area. Examples are drawn mainly from cold climate hydrology to illustrate the boundary issues but the questions transcend disciplinary areas. The intent of this presentation is to stimulate discussions that could be a prelude to finding solutions to many boundary problems which have thus far eluded hydrological investigations. Copyright © 2004 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".