COMMENTS ON THE PAPER ON POSSIBILISTIC ENTROPIES BY A. SGARRO AND L. P. DINU
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsVol. 10, No. 06, pp. 655-657 (2002) No AccessCOMMENTS ON THE PAPER ON POSSIBILISTIC ENTROPIES BY A. SGARRO AND L. P. DINUSILVIU GUIASUSILVIU GUIASUDepartment of Mathematics and Statistics, York University, Toronto, Ontario, M3J 1P3, Canadahttps://doi.org/10.1142/S0218488502001703Cited by:5 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles on Fuzzy Logic & Z-Numbers With a wide range of areas, you're bound to find something you like. FiguresReferencesRelatedDetailsCited By 5Possibilistic Coding: Error Detection vs. Error CorrectionLuca Bortolussi and Andrea Sgarro1 Jan 2010UTILITIES AND DISTORTIONS: AN OBJECTIVE APPROACH TO POSSIBILITIES CODINGANDREA SGARRO21 November 2011 | International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 13, No. 02An axiomatic derivation of the coding-theoretic possibilistic entropyAndrea Sgarro1 May 2004 | Fuzzy Sets and Systems, Vol. 143, No. 3Possibilistic Time Processes and Soft DecodingAndrea Sgarro1 Jan 2004Anticipating Extreme EventsMihai Nadin Recommended Vol. 10, No. 06 Metrics History Received 1 April 2002 PDF download
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it