Review of Rare Earth Elements as Fertilizers and Feed Additives: A Knowledge Gap Analysis
Bibliographic record
Abstract
Rare earth elements (REEs) are key constituents of modern technology and play important roles in various chemical and industrial applications. They also are increasingly used in agricultural and zootechnical applications, such as fertilizers and feed additives. Early applications of REEs in agriculture have originated in China over the past several decades with the objective of increasing crop productivity and improving livestock yield (e.g., egg production or piglet growth). Outside China, REE agricultural or zootechnical uses are not currently practiced. A number of peer-reviewed manuscripts have evaluated the adverse and the positive effects of some light REEs (lanthanum and cerium salts) or REE mixtures both in plant growth and in livestock yield. This information was never systematically evaluated from the growing body of scientific literature. The present review was designed to evaluate the available evidence for adverse and/or positive effects of REE exposures in plant and animal biota and the cellular/molecular evidence for the REE-associated effects. The overall information points to shifts from toxic to favorable effects in plant systems at lower REE concentrations (possibly suggesting hormesis). The available evidence for REE use as feed additives may suggest positive outcomes at certain doses but requires further investigations before extending this use for zootechnical purposes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".