Predicting the bioavailability of copper and zinc in soils: Modeling the partitioning of potentially bioavailable copper and zinc from soil solid to soil solution
Bibliographic record
Abstract
This research produced statistically based, semimechanistic models describing partitioning of Cu and Zn in 40 soils from the United States, Canada, the United Kingdom (UK), The Netherlands, and Chile with widely varying characteristics. Two different types of models were constructed, partitioning models and competitive adsorption models. Multiple linear regression (MLR) was employed to prioritize over 30 different soil characteristics. Multiple linear regression yielded equations predicting the partitioning of Cu and Zn. Equations were also created that estimated the potentially bioavailable fraction of Cu and/or Zn. Data from plant uptake studies (which are reported separately) governed the choice of a suitable chemical soil extraction that estimated bioavailable Cu (0.01 M HCl) and bioavailable Zn (0.01 M CaCl2). Soil pH (1:1 soil:deionized water [DI H2O]) and percent organic matter accounted for approximately 70% of the variability in Cu partitioning and 80% of the variability in bioavailable Cu in the 40 soils studied. For Zn, soil pH alone accounted for roughly 75% of the partitioning variability and 80% of the variability for the estimated bioavailable portion. The results presented here were used in conjunction with results from the plant uptake studies for the creation of models to assess the potential bioavailable metal associated with any given soil from a wide variety of locations.
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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) | 0.000 | 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".