Bibliographic record
Abstract
In the last lines of his poem “In Praise of Limestone”, W.H. Auden wrote, “[W]hen I try to imagine a faultless love or the life to come, what I hear is the murmur of underground streams, what I see is a limestone landscape”. When I was 12, my family moved to two acres of land underlain by Devonian limestone in Paignton, county of Devon, southwest England. At age 15, I began exploring the local caves – small, tight, but beautifully decorated. Later, studying physics at Cambridge, I found the University’s caving club and spent many happy weekends in and under the limestone areas of Britain. To quote Auden’s opening line in that poem, “If there is one landscape that we...are consistently homesick for, this is chiefly because it dissolves in water”. It was this property of limestone – the dissolution of it – that led me to complete a PhD in karst geomorphology at McMaster University (Canada) under the direction of Derek Ford, and later led to years of exploring caves in West Virginia, Tennessee, and the Canadian Rockies, and ultimately to a 43-year career as a professional geographer. But, it was only recently, and especially now in retirement, that I began to fully understand why caves, and geography as a discipline, have always held such a deep fascination for me.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".