Study of soil contamination by analyzing corroded lead bullets used for hunting in Cordoba, Argentina: A side effect climate change
Bibliographic record
Abstract
Deforestation due to agriculture expansion is menacing the north of Argentina, which is part of one large forested biomes of South America. Such expansion into forest areas is partially due to the increase in rainfall that has affected subtropical Argentina during the last decades. The rainfall increase is associated with an intensification of the continental circulation, “a process likely related to the global increase in CO2”. Some authors explored the relative importance of different factors influencing deforestation due to agriculture expansion relating them to climatic conditions and socioeconomic drivers 1 . As a consequence of agriculture expansion-deforestation process a huge hunting activity in the remnant forest surrounding the productive lands was developed in the north of the province of Cordoba. The region allows grain cultivation such as soybean, corn, sorghum and wheat. This food source is surrounded by native forest, and the combination of food source and roost has produced a population of Zenaida Auriculata (Paloma) estimated to be over 30 million birds. Every year it is expected thousands of dove hunters coming from USA, Europe, Canada, Arabian and Latin-American countries. Then, it is important to study the contamination of soils due to annual Pb deposition. A research track is to understand the oxidation and dissolution process of Pb occurring when the bullets fall to the ground with moderate impact energy. As long as the corroding bullets are present in soil, secondary Pb phases in the weathering crusts appears being an important source of bioavailable Pb. We are interested to describe the mineralogical composition and spatial distribution of Pb species around corroding ammunitions to understand the bioavailability of lead in the biosphere. Last year the environmental authorities of Cordoba regulated the dove hunting activity. Consequently our CEPROCOR established an agreement to measure the concentration of lead in hunting
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".