Characterization of River Sediments in Loja-Ecuador
Bibliographic record
Abstract
This research's main objective is to delineate areas with a high concentration of "pollutant" elements that imply a risk for the ecosystem and inhabitants' health in the Cordillera Real, south of Ecuador. To this end, a survey was carried out applying statistical analysis of the data. Specifically, the method of ordinary Kriging and Lepeltier is used to sort the data in populations according to their concentration. Previously, the information was compared with national (TULAS) and international regulations (Environmental Canada). These metals' spatial distribution is shown in concentration maps for each element (Hg, Pb, Zn, As, and Cu), where the potential villages exposed to these anomalies are displayed. In this sense, the samples' chemical digestion was conducted to quantify the atomic emission technique's before-mentioned pollutants' concentration. It was also correlated with geology, mineral occurrences, and metallogeny evidence to conclude that Pb and Zn anomalies are related to San Lucas granodiorite's intrusion, whereas Cu and Hg with local mineralization of sulfides, and as may be with domestic and industrial discharges. Finally, even though the anomalous concentrations of metallic elements were found to be characteristic of the lithology, cautions should be taken to safeguard the health of people and agriculture because there is evidence of elements such as As and Hg bioaccumulation in species that are part of the food chain.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".