Modeling surface complexation relationships in forest and agricultural soil
Bibliographic record
Abstract
The adsorption behaviour of trace metals in soil may provide us with a way to more accurately predict and assess the toxicity of metals in the environment. This thesis reports efforts to apply surface complexation modeling to agricultural and forest soil and to relate model parameters to common soil properties. This study considered Ca, Cd, Cu, Pb and Zn but the methods here could be applied to other metals. In Chapter 2, the surface charge and adsorption behaviour of a set of Ap horizons was characterised using back-titration and batch adsorption techniques. With the objective of simplifying the application of the NICCA model to surface charge and cation adsorption in whole soils the parameters of the NICCA model were related to soil properties (Chapter 3). Four of the six surface charge parameters could be predicted from soil properties and this enabled me to reasonably predict the surface charge of a second group of soils from soil properties. These results suggest that it is possible to make reasonable predictions about the surface charge and adsorption behaviour of a given type of soil using some easily measurable soil properties and a set of generic NICCA adsorption parameters for that soil type. In Chapter 4 this idea is applied to the determination of lime requirement for the agricultural 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".