Iron-organic matter colloid control rare earth element environmental mobility
Bibliographic record
Abstract
Rare earth elements (REE) have raised significant environmental concerns due to their increasing use in human activities and subsequent release into the environment. Hence, in the context of growing demand for “green” technologies and potential mismanagement of their life cycle, understanding their potential mobility within and between environmental compartments becomes crucial for evaluating their environmental risks. Colloids emerge as primary carriers/vectors facilitating REE mobility and transfer in the environment. This work addresses major topics related to the control exerted by colloids on the REE speciation and subsequent patterns. Among colloids, iron-organic matter colloids have been identified as the major REE carrier in surface water under various pedoclimatic conditions. Compelling evidences were provided that the mixing of iron-, organic- and iron-organic colloids could explain both REE concentration and pattern under environmental conditions. However, there is currently a lack of data on the specific distribution of REE between the iron and organic matter phases within Fe-OM colloids. It remains unclear whether REE distribution is primarily controlled by colloid mixing since structural rearrangements of Fe-OM colloids under varying hydrological and physicochemical conditions exert also a significant role.
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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 | 0.001 | 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".