Adsorption of the herbicide terbuthylazine across a range of New Zealand forestry soils
Bibliographic record
Abstract
The growing importance of environmental certification in plantation forestry is increasing the pressure to discontinue the use of chemicals including herbicides classified as highly hazardous. One of these herbicides is terbuthylazine, a triazine used widely to control a broad range of weeds in plantation forests in New Zealand. Using soil samples obtained from a national trial series, the key objectives of this study were to (i) determine the variation in soil adsorption of terbuthylazine as measured by distribution constant (Kd) across a range of soil types and (ii) develop a multiple regression model to predict Kd from key soil chemical properties. Across the 34 sampled sites, Kd averaged 21.9 L·kg–1 and ranged 38-fold from 3.9 to 146.7 L·kg–1. There was a highly significant (P < 0.0001) relationship between organic carbon and Kd, which was best described by a power function, that explained 86% of the variance in Kd. Addition of pH to the model using an exponential decay function was significant and increased the R2 for the model to 0.99. Across soil orders, Kd varied significantly by 10-fold. Values of Kd were lowest on Raw and Recent soil orders, which have low soil organic carbon and a relatively high pH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".