Thermodynamic Database Update to Model Synthetic Chelating Agents in Soil Systems
Bibliographic record
Abstract
Poliaminocarboxylate and polyaminophenolcarboxylate chelating agents, being the most representatives EDTA and o,o-EDDHA, have been profusely studied by our research team during the last 25 years because they are synthetized to be mainly used as micronutrient fertilizers to correct nutritional disorders affecting largely on crop yields placed under Mediterranean conditions. In the last years new chelating agents were designed and synthesized and the most of them were proposed to be included in the current European Directive on Fertilizers. Overall chelating agent properties, including equilibrium in soil by modeling, should be taken in account in order to check the iron chlorosis correction ability. Chemical speciation programs such as MINTEQA2, and most recently VMinteq, are being successfully used as tools to predict the behavior of each novel chelating agent in soil-plant system. Nowadays just one polyaminophenolcarboxylate chelating agent (o,o-EDDHA) is available into a VMinteq-compatible database (Lindsay's database) whereas more than seven of these type of products are authorized by European fertilizers normative to be used as micronutrient fertilizers. Therefore the aim of this work was the database updating to include all chelating agents related to o,o-EDDHA and EDTA whose complete characterization is performed and published elsewhere. Once database is updated, further modelization studies such as equilibrium reactions and adsorption isotherms with solid phase may be readily performed to get fundamental information and understand the reactivity of these recalcitrant polyaminophenolcarboxylates in soils.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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".