Bibliographic record
Abstract
The mining industry is one of the sectors identified by government to spur growth in the economy. It is projected that over the next five years, mining would grow by an average of 13 percent 1 following the Mineral Action Plan, which provides the framework for the development of the country’s vast mineral resources. Of the country’s total land area of 30 million hectares, about 30 percent is geologically prospective for metallic minerals. The industry received a big boost last December 2004 when the Supreme Court (SC) ruled the Mining Act of 1995 as constitutional. Large investments are expected to pour in because of the SC decision. Foreign firms are now allowed to own 100 percent of mining operations where the operations are under a financial or technical assistance agreement (FTAA) and the project investment is greater than US$50 million. The impact of the SC decision appears to have already borne fruit. The gross value added from mining and quarrying has gone up by 14 percent in the second quarter of 2005 from its level in the same period in 2004. The growth of the sector was primarily induced by the infusion of investments in the Palawan Nickel Project of Coral
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".