Bibliographic record
Abstract
Over the period from 1985 to 2005, BHP carried out three major geophysical research projects that were intended to significantly enhance the ability to find new ore deposits. This involved major activities internally as well as external components that involved complex multi-year programs involving large expenditures. In all cases, major efforts were made to deploy the outcomes in BHP’s minerals exploration programs. While the technologies developed could be considered as successful in having met or exceeded the original development goals, in no case did the outcomes of these efforts contribute materially to the discovery of significant new mineral resources. This suggests that the technical objectives for a new technology can be comparatively straight-forward to define, but the subsequent implementation path, once the technological goals are achieved, were poorly conceived.BHP’s experience is much like the exploration industry as a whole over the same period. While much appears to have been developed which has added significantly to the technical capabilities of the industry, it has been less apparent that these developments have been able to contribute significantly to an improved discovery record.Considerable effort is now being directed towards bringing on new geophysical technologies in programs such as Uncover in Australia and CMIC’s Footprint in Canada. Past experience suggests, however, that better technology alone can’t be expected to achieve the sought after goal of improved discovery success.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".