Bibliographic record
Abstract
Reviewed by: The Secret Cave: Discovering Lascaux Elizabeth Bush McCully, Emily Arnold . The Secret Cave: Discovering Lascaux; written and illus. by Emily Arnold McCully. Farrar, 2010. 40p. ISBN 978-0-374-36694-0 $16.99 Ad 6-9 yrs. Here McCully offers a "fictional recreation based on anecdotal accounts" of how the Paleolithic cave paintings at Lascaux, France were discovered in 1940 by a quartet of local boys, and possibly a dog named Robot. The eldest boy, teenager Marcel Ravidat, is dismayed to see his dog disappear down a hole near a tree root and, in retrieving him, believes he has found a tunnel that once led to a nobleman's chateau. Three other boys offer to help Marcel explore the tunnel, and over several subsequent visits, they locate a series of passages and chambers covered with what one of the boys recognizes as ancient paintings. As word of the discovery leaks out among the locals, the boys consult Marsal's teacher, who finally visits the cave, confirms their theory, and helps them hand the discovery over to experts. This plausible version of the event would be more gripping if the dialogue among players were [End Page 84] more natural and convincing: "'That's it,' said Marcel. 'The count's treasure isn't here.' 'The treasure is all over the walls,' Jacques cried." And teacher Monsieur Laval stiffly and heroically exclaims, "'This treasure comes straight from our ancestors to all people everywhere!'" Apocryphal though it may be, the account should be a serviceable introduction for young listeners, with luscious watercolor renderings of the cramped, inky passageways vying for oohs and aahs with recreations of the startlingly realistic animal paintings themselves. A substantial final note is included, accompanied by a black-and-white photograph of Laval and the boys in the cave as well as a bibliography of secondary sources. Copyright © 2010 The Board of Trustees of the University of Illinois
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".