Bibliographic record
Abstract
Statistical Computing, An Introduction to Data Analysis Using S-Plus. Michael J. Crawley. Chichester, UK: Wiley UK, 2002 (reprinted, with corrections, March 2003), 772 pp., $85.00, hardcover. ISBN 0-471-56040-5. The Statistical Evaluation of Medical Tests for Classification and Prediction. Margaret S. Pepe. New York: Oxford University Press, 2003, 320 pp., $115.00, hardcover. ISBN 0-19-850984-7. Introductory Biostatistics. Chap T. Le. Hoboken, NJ: Wiley-Interscience, A John Wiley & Sons Publication, 2003, 572 pp., $94.95, hardcover. ISBN 0-471-41816-1. You always need to know ten times as much as you use —Quoted by E.A. Murphy in Biostatistics in Medicine (1982) Teaching data analysis is not easy, and the time, allowed is always far from sufficient —J.W. Tukey (1962) As junior medical students in Glasgow in the 1950s, we learned statistics from a poor lecturer and an excellent book (M.J. Moroney, Facts from Figures , Penguin, 1951). Unfortunately this was before the availability of calculators, and therefore, no effort was made to use these techniques to examine the results we were currently producing in the practical classes of physiology and biochemistry. We therefore benefited little from that early exposure except to remember the t -distribution because of its association with beer. I thought of Moroney’s book when I opened Crawley’s Statistical Computing . Both volumes are written in lively style; are lucid, comprehensive, and rigorous; and are illuminated throughout with flashes of dry humor. Crawley is a distinguished ecologist and a member of the Department of Biological Sciences at Imperial College in London, England, and has been involved in the teaching and research applications of statistical techniques for many years. One difference between Facts from Figures and Statistical Computing is now the ready availability of the computer, which allows a reader of the latter text to examine data and assess their statistical significance while using the …
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 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 | 0.001 | 0.050 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".