Barrick Appoints Alex Davidson Executive Vice President, Exploration Reflects Company's Increasing Focus on Exploration
Bibliographic record
Abstract
Barrick Gold Corporation today announced the appointment of Alex Davidson as Executive Vice President, Exploration. The appointment reflects Mr. Davidson’s contributions to the Company, formerly as Senior Vice President, and Barrick’s increasing strategic focus on exploration. Since joining Barrick in 1993, Mr. Davidson has overseen the expansion of the Company’s exploration program from North America into South America and around the globe. “Under Alex’s direction, our exploration teams have had a number of world-class exploration successes including the Alto Chicama gold discovery in Peru in 2002,“ said Greg Wilkins, President and Chief Executive Officer. Alto Chicama resulted from Barrick’s decision to undertake an expanded grassroots exploration program as industry exploration declined. Mr. Davidson recently received the Prospector of the Year Award from the Prospectors and Developers Association of Canada. Prior to joining Barrick, Mr. Davidson explored on behalf of major mining companies in Canada and Latin America. He graduated from McGill University in 1976 with a Masters degree in Economic Geology. Barrick is one of the world’s leading gold producers, with operations and development projects on four continents, one of the largest gold reserves, at 87 million ounces, and the industry’s only A-rated balance sheet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.146 | 0.038 |
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".