Published by Canadian Center of Science and Education 193 Effects of Sand/Gravel Mining in Minna Emirate Area of Nigeria on
Bibliographic record
Abstract
The paper examined sand and gravel mining activities both on land and the rivers as a business venture in Minna emirate council of Niger state, Nigeria. It identified various stakeholders in this business as: the landlords of the quarries, the local government authorities and the miners among others. It further looked at what each stakeholder stands to gain or lose in the business. Quantitative data were collected on the direct financial benefits from the quarrying work and the analysis of these data, using percentages, showed that while the quarry owner and the local government put together earn less than 8 percent of the total profit accruing from the business, the miner ferries away over 92 percent of the accrued revenue. It was recommended that repairs should be made to the exploited mines and that government agents in charge of the quarries should be more responsive to the movements in the market prices of the commodity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.638 | 0.199 |
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".