Chapter 15 Collaborating on the Skiddaw Group
Bibliographic record
Abstract
Abstract When the Lakeland Project started in 1982, the first Survey men to go into the field among the outcrops of the Skiddaw Slates were Peter Allen, Tony Cooper, Adrian Rushton and Stewart Molyneux, and they were soon joined by Barry Webb, with his previous Lakeland experience. Later, Philip Stone did a good deal of Skiddaw work as he had experience of somewhat analogous rocks in the Southern Uplands, and he is currently (2001) leader of the Lakeland Project. 1 Richard Hughes (see Fig. 14.3), a former student of Barrie Rickards at Cambridge, did a considerable amount of Skiddaw work after he joined the Survey group at the Newcastle office in 1989, and undertook the scientific editing of the Survey's Skiddaw Memoir, currently in press. 2 The surveyor Eric Johnson (see p. 209 and Fig. 14.3) did work on the Black Combe area, as did Andrew Bell from the Open University on independent contract to the Survey, 3 and Soper's Sheffield PhD student Neil Mathieson, co-supervised by Peter Allen, did work in the area too, but more specifically on the volcanic rocks. 4 Co-supervised by Soper and Cooper, Richard Moore from Leeds University did important work for his PhD (1992), which influenced the interpretations of the Skiddaw Group in northern Lakeland. However, broadly speaking, the input from university geologists to the Lakeland Project has been less important for 'Otley F than for 'Otley IF and TIF. Cooper joined the Survey in 1975. 5 Having worked on Ordovi-cian rocks for his PhD, he expected to be put
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.109 | 0.032 |
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".