Estimating the Relative Leaching Potential of Herbicides in Alberta Soils
Bibliographic record
Abstract
Abstract Our objective was to use a simple screening model to predict the relative leaching of herbicides in Alberta soils to allow producers the option of choosing herbicides with lower leaching potential. Physical properties for each herbicide were obtained from the literature and the Laskowski model was used to calculate the leaching potential (LP) of the herbicides. Relative LP rankings (LPR) were then created by ranking herbicide LP values on a 1 to 9 scale (1 = no leaching; 9 = high leaching). The leaching rates of nine herbicides (2,4-D, dicamba, MCPA, diclofop, quinclorac, bromoxynil, fenoxaprop, triallate and trifluralin) were then determined on soils from the five major soil zones of Alberta (Brown, Dark Brown, Grey, Dark Grey and Black) using small packed soil columns. Eluate fractions were analyzed using a MSD-GC method. Although there were differences related to soil organic matter content, the relative rates of leaching among the nine herbicides were generally quite consistent. Dicamba, 2,4-D, MCPA and quinclorac leached most readily, followed by bromoxynil, and then diclofop, fenoxaprop and triallate, and finally trifluralin, which did not leach. These soil column results and previous field results validated the LPR for most of the nine herbicides. The LPR did appear to underestimate the leaching of MCPA, bromoxynil and quinclorac. LPR values (1 to 9 scale) are a convenient way to convey herbicide leaching information to producers and could easily be included in herbicide guides along with certain provisos.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".