Bibliographic record
Abstract
Book Review| December 01, 2005 Fuzzy Logic in Geology: Edited by Robert V. Demicco and George J. Klir Zhuoheng Chen Zhuoheng Chen 1Geological Survey of Canada (Calgary), 3303-33 Street NW, Calgary, AB, T2L 2A7 Search for other works by this author on: GSW Google Scholar Bulletin of Canadian Petroleum Geology (2005) 53 (4): 498–499. https://doi.org/10.2113/53.4.498 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Zhuoheng Chen; Fuzzy Logic in Geology: Edited by Robert V. Demicco and George J. Klir. Bulletin of Canadian Petroleum Geology 2005;; 53 (4): 498–499. doi: https://doi.org/10.2113/53.4.498 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of Canadian Petroleum Geology Search Advanced Search Fuzzy Logic in Geology. 2004. Robert V. Demicco and George J. Klir (Eds.). Elsevier Academic Press, 347p. Price: $95 USD. For quite some time, the engineering community has enjoyed success in application of fuzzy logic, and has appreciated the resultant progress in understanding domain problems through its use. Most of us in the geological community perhaps did not fully recognize the impact of fuzzy logic in geology until recently, when Fuzzy Logic in Geology, edited by Professors Robert V. Demicco and George J. Klir, was published. Fuzzy set theory was introduced in 1965 by Proffesor Lotfi Zadeh of... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".