Bibliographic record
Abstract
Aral Sea 5 Arctic Circle 4 armouring 70, 71, 72, 73 -4, 75, 81 river beds 227 Arnardalsá (Iceland) 41 Arnardalsalda (Iceland) 38 Artvin (Turkey) 206 Arun Valley (Himalayas) 291, 299, 300 Arundel Bridge (New Zealand) 102, Plate 1 Ásbyrgi (Iceland) 37, 38, 39 -41, 42-3, 48 -9 canyon 37 Ashburton River (New Zealand) 99, 101, 103, 105, Plate 3 Asia 3, 5, 12, 291 flooding 7 see also Central Asia Askja (Iceland) 38 Astoria Canyon (Pacific) 5 Astoria Fan (Pacific) 5, 12 Atlantic 4, 9 Australia 104, 177, 259 Axarfjörbur (Iceland) 38 -9, 41, 48, 49 backscatter 269, 270 -1, 272 -3, 275 -7, 278 backwater effects 78, 80, 81, 94 Bahamas 9 Baikal, Lake (Russia) 19 Barajas Airport (Spain) 241 Baratel (Siberia) 25 Bárbarbunga caldera (Iceland) 37, 38 Barruelos Bridge (Spain) 236, 237-8, 239 bar(s) 177, 246, 259, 261, 266 braid 236 channel 236 deposition 17, 27 deposits 219 -25 development 183 -4 flood-generated 181 and flooding 227-9 giant 17, 24, 25 -9, 30, 32-3, 44 lateral 240 migration 235, 238, 247 morphology 218 -19 scroll 240, 243, 245 sediment dispersal 225 -7 stacked 196 surficial features 220 -5 see also chute bars; flood bars; gravel bars; side bars Bashkaus (river) 18, 20 Flood and Megaflood Processes and Deposits: Recent and Ancient Examples.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.781 | 0.694 |
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".