2,4-D mineralization in unsaturated and near-saturated surface soils of an undulating, cultivated Canadian prairie landscape
Bibliographic record
Abstract
The herbicide 2,4-D [2,4-(dichlorophenoxy) acetic acid] is one of the most widely used pesticides in the Canadian prairies and is frequently detected as a ground and surface water contaminant. The objective of this paper was to determine the magnitude and extent of variation of 2,4-D mineralization in a cultivated undulating prairie landscape. Microcosm incubation experiments, using a 4 x 3 x 2 factorial experimental design (soil moisture, 4 levels: 60, 85, 110, 135% of field capacity; slope position, 3 levels: upper-, mid- and lower-slopes; soil depth, 2 levels: 0-5 and 5-15 cm), were used to assess 2,4-D mineralization. The first-order mineralization rate constant (k(1)) varied from 0.03 to 0.22 day(- 1), while total 2,4-D mineralization varied from 31 to 52%. At near-saturated conditions (110 and 135% of field capacity), the onset of 2,4-D degradation was delayed in soil obtained from the upper- and mid-slopes but not in soils obtained from the lower-slope position. The k(1) and total 2,4-D mineralization was significantly influenced by all three factors and their interactions. The Freundlich sorption coefficient of 2,4-D ranged from 0.83 to 2.46 microg (1-1/n)g(- 1) mL(1/n) and was significantly influenced by variations in soil organic carbon content across slope positions. The infield variability of 2,4-D sorption and mineralization observed across slope positions in this undulating field was comparable in magnitude and extent to the regional variability of 2,4-D sorption and mineralization observed in surface soils across Manitoba. The large variability of 2,4-D mineralization and sorption at different slope positions in this cultivated undulating field suggests that landform segmentation models, which are used to delineate slope positions, are important considerations in pesticide fate studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".