Automatización de la separación desmonte - mineral Ore Sorter en Mina Papagayo
Bibliographic record
Abstract
Compañía Minera Poderosa SA (CMPSA) at its center of operations in the area Pataz, Libertad department currently is operating in the mining of different mining operations, for processing in the Marañón Plant and Plant Santa Maria, using a transport system dump mine plant with capacity of 24 MT. \n\tThe company has a defined operation based on all the years that it is performing its duties, but following its policy of continuous improvement, has been proposed to increase the efficiency of Mina, for which want to reduce dilution in the exploitation phase separating material has no values, in order to increase the head grade of the ore to be transported to the processing plant. \n\tTo this end, it is intended that an automatic machine with German technology called Ore Sorter to operate meeting the requirements of capacity and quality in the area of the mine to reach more than 65 MT / hour mineral in the diet treatment. \n\tTo fulfill this purpose has contacted the German company Commodas UltraSort who manufacture equipment to treat different types of materials. Currently are working in different countries with mining operations marked success. Reference must be Brazil, Canada, Russia, Australia, USA and Austria among others are working with iron ore, gold, non-metallic and polymetallic. Based on the available information has been made a follow-up, coordination and selection of equipment which might be used in operations CMPSA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".