Bibliographic record
Abstract
Environmental Management and Change in Plymouth and the South West. By Mark Blacksell, Judith Mathews and Peter Sims. People, Environment, Disease and Death. By Melvyn Howe. Timing global cities. Edited by S.G.E. Gravesteiin, S. van Griensven and M.C. de Smidt Modern Geographical Thought By Richard Peet. The Political Ecology of Bananas: Contract Farming, Peasants, and Agrarian Change in the Eastern Caribbean. By Lawrence S. Grossman The Holocene: an Environmental History. By Neil Roberts Histoire de la Géographie Franáise de 1870 à nos jours. By Paul Claval Battling the Elements, Weather and Terrain in the Conduct of War. By Hrold A. Winters with Gerald E. Galloway, William I. Reynolds and David W. Rhyne The Military Use of Land, A History of the Defence Estate. By John Childs Flood Studies in India. Edited by Vishwas S. Kale The Northridge Earthquake: Vulnerability and Disaster. By Robert Bolin with Lois Stanford The Assessment of the Status of Human Induced Soil Degradation in South and Southeast Asia. By G.W.A. van Lynden and L.R. Oldeman Asia Pacific: New Geographies of the Pacific Rim. Edited By F.R. Watters and T.G. Mcgee
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.462 | 0.330 |
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".