Bibliographic record
Abstract
Abstract. Streams are defined by the presence of a streambed, which is a linear depression where water flows between discernible banks. The upstream boundary of a stream is called a channel head. Headwater streams, which are small streams at the top of a watershed, account for the majority of the total length of streams, yet their exact locations are still not well known. For years, many algorithms were used to produce hydrographic networks that represent headwater streams with varying degrees of accuracy. Although digital elevation models derived from LiDAR have significantly improved headwater stream detection, the performance of the algorithms with different geomorphic characteristics remains unclear. Here, we address this issue by testing different combinations of algorithms using classification trees. Homogeneous hydrological processes were identified through hydrological classification. The results showed that in shallow soil that mainly consists of till deposits, the algorithms that recreate the surface runoff process provide the best explanation for the presence of a streambed. In contrast, streambeds in thick soil with high infiltration rates were primarily explained by a small-scale incision algorithm. Furthermore, the use of an iterative process that recreates water diffusion made it possible to more accurately detect streambeds than other methods tested, regardless of the hydrological classification. The method developed in this paper shows the importance of considering hydrological processes when aiming to identify headwater streams.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.020 | 0.009 |
| Insufficient payload (model declined to judge) | 0.231 | 0.197 |
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".