Bibliographic record
Abstract
“ … geostatistics is of real potential if it is reconciled with the geology of the deposit” (King et al., 1982). Resource/reserve estimation is too often viewed as a set of recipes when it is in reality an intellectual undertaking that varies from one case to another. Even the estimation of two different deposits of the same type can involve significantly different procedures. We undertake an estimation study with limited information that represents perhaps 1/100,000 of the deposit in question. The information base, however, is larger than we sometimes think, including not just general geology and assay data, but information from other sources such as sampling practice, applied mineralogical studies, geophysical and geochemical survey data, and a range of engineering information – all of which contribute to the development and implementation of an estimation procedure. The use of such varied data demands that the opinions of a range of experts be taken into account during estimation. Increasingly, it is becoming more difficult for a single person to conduct a comprehensive resource/reserve estimation of a mineral deposit. Clearly, all sources of information must be considered in reasonable fashion if a mineral inventory study is to be professionally acceptable; it is morally unacceptable to provide an estimate that is seriously in conflict with pertinent data. Of course, it must be recognized that even with the best of intentions, procedures and abilities, errors will be made. After all, 99,999/100,000 of the deposit must be interpreted from widely spaced control sites.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.372 | 0.230 |
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".