An analytical framework to decode socioeconomic interplays in pesticides and fertilizer container collection patterns using land dynamics metrics
Bibliographic record
Abstract
• Contrast variations in EPFC and NRBC collections from 2016 to 2022 by CleanFarms. • Developed agriculture regions showed higher and consistent collection coverage. • Economic stability enhances EPFC collection efficiency in developed regions. • Emerging regions excel in land utilization collection but lag economically. • Developed MLR models highlight profitability and labor as key efficiency drivers. This study analyzes pesticide and fertilizer container collection trends across Canadian agricultural regions over a seven-year period from 2016 to 2022 through an analytical framework and proposed two land metrics. A 28.3 % decrease in the collection of small empty pesticide and fertilizer containers (EPFCs) coincides with a 41.4 % increase in the collection of non-refillable bulk containers (NRBCs) among associated businesses, indicating a trend toward larger containers, influenced by economic incentives and regulatory guidelines. Nine Canadian provinces were into two regions (developed and emerging) based on their agricultural activities. The agricultural stewardship organization’s spatial collection coverage ratios were notably higher in the developed regions (0.003 to 0.010) than in the emerging ones (0.001 to 0.006), suggesting that recycling services are more efficient in areas with intense agricultural activity. The median EPFC collection rates varied significantly, with the developed regions showing more stability and higher densities (0.24 to 0.41 containers per million CAD) than the emerging ones (0.12 to 0.27 containers per million CAD). The emerging regions exhibited higher land use collection ratios, while the developed regions reported significantly lower ratios, reflecting the challenges posed by larger farm landscapes. The developed collection regression models (R 2 = 0.82 to 0.89 and p < 0.0001) highlighted labor and economic factors as predictors of collection efficiency in both regions. These findings indicate that stronger economic incentives and focused infrastructure upgrades could enhance EPFC collection efficiency, especially in the less developed agricultural areas. Targeted policies that enhance collection infrastructure and integrate labor and economic factors to improve stewardship efficiency and support environmental sustainability are recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".