Bibliographic record
Abstract
1. Rivers are subject to thresholds of several types that define significant changes in processes and morphology and delimit distinctive riverine landscapes and habitats. Thresholds are set by the conditions that govern river channel process and form, amongst which the most important are the flow regime, the quantity and calibre of sediment delivered to the channel, and the topographic setting (which determines the gradient of the channel). These factors determine the sediment transport regime and the character of alluvial deposits along the channel. 2. Changes occur systematically along the drainage system as flow, gradient and sediment character change, so a characteristic sequence of morphological and habitat types – hence of riverine landscapes – can be described from uplands to distal channels. The sequence is closely associated with stream competence to move sediment and with bank stability. 3. The paper proposes a first order classification of river channel and landscape types based on these factors. The riverine landscape is affected seasonally by flow thresholds, and further seasonal thresholds in northern rivers are conditioned by the ice regime. 4. It is important to understand geomorphic thresholds in rivers not only for the way they determine morphology and habitat, but because human activity can precipitate threshold crossings which change these features significantly, through either planned or inadvertent actions. Hence, human actions frequently dictate the character of the riverine landscape.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".