Bibliographic record
Abstract
Contributors. Preface. 1 Sediment Cascades in the Environment: An Integrated Approach(Timothy P. Burt, Durham University, UK and Robert J. Allison,University of Sussex, UK). 2 Mountains and Montane Channels (Michael Church, Universityof British Columbia, Canada). 3 Landslides and Rockfalls (Nick J. Rosser, DurhamUniversity, UK). 4 Sediment Cascades in Active Landscapes (Tim R. H. Davies,University of Canterbury, New Zealand and Oliver Korup, SwissFederal Institute for Forest, Snow and Landscape Research). 5 Pacific Rim Steeplands (Basil Gomez, Indiana StateUniversity, USA Michael J. Page, GNS Science, New Zealand Noel A.Trustrum, GNS Science, New Zealand). 6 Local Buffers to the Sediment Cascade: Debris Cones andAlluvial Fans (Adrian M. Harvey, University of Liverpool,UK). 7 Overland Flow and Soil Erosion (Louise J. Bracken, DurhamUniversity, UK). 8 Erosional Processes and Sediment Transport in Upland Mires(Martin G. Evans, University of Manchester, UK and Timothy P.Burt, Durham University, UK). 9 Gravel-Bed Rivers (Michael Church, University of BritishColumbia, Canada). 10 The Fine-Sediment Cascade (Pamela S. Naden, Centre forEcology and Hydrology, UK). 11 Streams, Valleys and Floodplains in the Sediment Cascade(Stanley W. Trimble, University of California at Los Angeles,USA). 12 Lakes and Reservoirs in the Sediment Cascade (Ian D.L.Foster, University of Westminster, UK). 13 Continental-Scale River Basins (David L. Higgitt, NationalUniversity of Singapore). 14 Estuaries (Tom Spencer, Cambridge University, UK andDenise J. Reed, University of New Orleans, USA). 15 The Continental Shelf and Continental Slope (David N.Petley, Durham University, UK). Index.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".