Fertilizer Impacts on Cadmium Availability in Agricultural Soils and Crops
Bibliographic record
Abstract
Ingestion in food is a major pathway of cadmium (Cd) exposure for humans. It is therefore desirable to ensure that Cd concentrations in crops that enter the human food chain do not increase to levels that may lead to health risks. Phosphorus fertilizers contain Cd as a contaminant at levels varying from trace amounts to as much as 300 mg Cd kg–1 of dry product and therefore can be a major source of Cd input to agricultural systems. Fertilization can influence Cd accumulation in crops by direct Cd addition and by indirect effects on soil pH, ionic strength, Zn concentration, rhizosphere chemistry, microbial activity, and plant growth. Cadmium will accumulate in the soils from fertilizer applications if the amount of Cd added in fertilizer is greater than the amount of Cd removal, whether in harvested crop removal or other loss pathways such as leaching, erosion, or bioturbation. Assessment of the impact of fertilizer management practices on the risk of Cd toxicity to the soil ecosystem and the risk of movement of Cd into the human diet must consider both the direct influence of Cd addition as a fertilizer contaminant and the indirect effects of fertilizer application on Cd phytoavailability. Cadmium accumulation in soils and crops can be minimized by adoption of management practices that improve fertilizer-use efficiency while minimizing Cd input.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".