Concentración de metales pesados en especies vegetales utilizadas para la remediación de relaves en compañía minera Colquirrumi (Tesis parcial)
Bibliographic record
Abstract
ABSTRACT \n \n \nThe research was conducted at the Colquirrumi mining unit located in the department of Cajamarca, \nprovince and district of Hualgayoc. In this place Compañia Minera Colquirrumi closed up 04 deposits \nof mine tailings with an encapsulation technique, which consisted of ensuring its physical and \nchemical stability, hydrology of the place, runoff water management. \nThe general objective of this thesis was to evaluate the degree of concentration of heavy metals in \nplant species (Ray Grass and Red Clover) that were cultivated in the closure of tailings deposits and \nplant species that grew in areas adjacent to tailings deposits. but that they were not exposed to the \nmining intervention. \nFor this we identify the areas of remediated mine tailings and the areas not affected by the CMC \nmining activity, we determine the concentration of heavy metals (Copper, Lead and Zinc) in growing \nplants in tailings and in natural soils and we determine the translocation coefficient between the aerial \npart of the plant and roots. \nThe results showed that the Lead and Zinc reported in the species that grew in the tailings had lower \nconcentration of metals than those species that grew in natural soils, this would imply a good indicator \nof the type of closure made in the mining unit. The copper results showed that there is a higher \nconcentration in the species that grew in the tailings (180 ppm), however, this concentration does \nnot exceed the maximum permissible limits established in Australian or Canadian law. \nFinally, this work shows at a general level that the levels of metals that were found in the closure of \nmines are lower than those reported by natural soils, however, more research must be done.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".