Protecting Farmland Pollinators: Whole Farm Scorecard - Experiences and Recommendations
Bibliographic record
Abstract
Protecting Farmland Pollinators is about identifying small actions that farmers can take that will allow biodiversity to coexist within a productive farming system. Farmers in Ireland recognise the importance of pollinators, but farmland has experienced wide-scale loss of wild pollinators over the last fifty years. By working closely with 40 farmers, management practices that benefit bees and hoverflies on Irish farmland were identified, and a whole farm pollinator scoring system was developed. Using a whole farm pollinator scorecard, farmers receive ‘pollinator points’ each year based on the amount and quality of pollinator friendly habitat maintained and/or created and, each year, farmers receive a results-based payment that relates to the points. Irish farms have great potential to improve both the quantity and quality of biodiversity friendly habitats without negatively impacting on farm productivity. Thirty-one farmers increased their score between year one and year three of the results-based payment and four farms more than tripled their score. The median whole farm pollinator score for the 40 farms increased from 25,696 in year one to 33,572 in year two (31% increase), to, 40,211 pollinator points in year three (56% increase). Each farm type (beef, dairy, mixed and arable) increased their median score over the three years and dairy and arable farms showed the largest increase. This project has helped farmers better understand and engage with nature on their land and has created a measurable system for improving habitats for biodiversity on farms that is accessible to all and has the potential to be rolled out on a wider scale.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".