Evaluation of affinity constants of Cu, Cd, Ca and H for active soil surfaces for a solid phase-controlled soil ligand model
Bibliographic record
Abstract
Environmental context. The speciation of metals in soils is controlled by the equilibrium between the solid and aqueous phases and by several parameters such as pH and total metal concentrations. The integration of affinity constants between several cations and active soil surfaces of different soils in the chemical equilibrium modelling software MINEQL+ allows a good evaluation of the chemical speciation of the metals. Abstract. A new approach, derived from the concept of the biotic ligand model, was used for the determination of the affinity constants of Ca, Cu, Cd and H to the active surfaces of different kinds of soils. This approach allowed us to obtain consistent data and to integrate these values in the chemical equilibrium modelling software MINEQL+ and eventually into a solid phase-controlled soil ligand model. This could then very easily be transformed into a terrestrial biotic ligand model by adding constants for biological components. We obtained the chemical speciation of the metals of interest by integrating the initial characteristics of the soil (pH; cation exchange capacity, CEC; total metal concentrations in soil extracts; ionic strength; and CO2 pressure). Comparison of the predicted and measured values of free Cu2+ is excellent using soil-specific affinity constants as well as average values. The average affinity constants between the active soil surfaces (S) and the target cations are log KCa–S = –0.84 (±0.01), log KCu–S = 5.3 (±0.1), log KCd–S = 4.4 (±0.2) and log KH–S = 4.1 (±0.2). External soils have been used to validate the conceptual model and the results show a very good correlation between the predicted and the measured free Cu (pCu) except for an acidic soil (pH < 5.2), highlighting the importance of integrating Al into the model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".