Yield to the Data: Some Perspective on Crop Productivity and Pesticides - Excel user form
Bibliographic record
Abstract
The hectares of habitat protected and the number of adults and children fed in one year were calculated for each of the six crop types for Canada and United States. The calculations were based on the 50th centile of the cumulative frequency distributions of change in crop yield due to pesticide treatment for each crop type. An editable interactive table was created using Microsoft Excel that would allow individuals to determine how pesticide treatment in their selected jurisdiction (province in Canada or state in the United States) and crop translates into habitat saved, calories produced, and mouths fed. This table allows the user to choose the country (Canada or United States), whether to include the organic agriculture correction factor, their state or province of interest, crop, and whether a young child, adolescent child, adult women, or adult man is being fed. The table will then calculate the hectares of habitat saved, added number of calories produced (kcal), the number of individual fed in one day, and the number of individual fed in one year. Due to the variability in yield results between crops and studies, the Excel user form allows individuals to set whichever yield increase they anticipate observing or use the 50th centile of yield increase from the cumulative frequency distribution for each crop.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.060 |
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".