Discussion on 'Essential basic concepts in mining geostatistics and their links with geology and classical statistics' (S. Afr. J. Geol., 102, 147--152)
Bibliographic record
Abstract
Other| March 01, 2000 Discussion on ‘Essential basic concepts in mining geostatistics and their links with geology and classical statistics’ (S. Afr. J. Geol., 102, 147—152) F.T. Manns F.T. Manns Artesian Geological Research, 106 Scarborough Road, Toronto, Ontario, M4E 3M5 Canada, E-mail: artesian@netcom.ca Search for other works by this author on: GSW Google Scholar South African Journal of Geology (2000) 103 (1): 97–98. https://doi.org/10.2113/103.1.97 Article history first online: 07 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation F.T. Manns; Discussion on ‘Essential basic concepts in mining geostatistics and their links with geology and classical statistics’ (S. Afr. J. Geol., 102, 147—152). South African Journal of Geology 2000;; 103 (1): 97–98. doi: https://doi.org/10.2113/103.1.97 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 SocietySouth African Journal of Geology Search Advanced Search Sixteen articles were carried in the Northern Miner in Canada over nine months in 1998—99, supporting or refuting the methodology of geostatistics in one aspect or another. The appearance of such articles has slowed, but is certain to begin again, because the discussion has never evolved into a high enough level of dialogue. The crux of the problem appears to be the various good and bad experiences concerning reliability. The erratic results of geostatistics as we understand them, with their supporters and detractors, strongly suggest to some of us that industry standard split drill core samples are too small to... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".