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Record W2789952326 · doi:10.1071/aseg2018abw8_2d

Quest for the Holy Grail; BHP’s Geophysical Research Program 1985-2005

2018· article· en· W2789952326 on OpenAlexaffabout
Ken Witherly

Bibliographic record

VenueASEG Extended Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsCondor Petroleum (Canada)
Fundersnot available
KeywordsHoly GrailPeriod (music)Computer scienceBusinessEngineering managementData scienceEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.359
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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