Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it