Ecological intensification and diversification approaches to maintain biodiversity, ecosystem services and food production in a changing world
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do we redesign agricultural landscapes to maintain their productivity and profitability, while promoting rather than eradicating biodiversity, and regenerating rather than undermining the ecological processes that sustain food production and are vital for a liveable planet? Ecological intensification harnesses ecological processes to increase food production per area through management processes that often diversify croplands to support beneficial organisms supplying these services. By adding more diverse vegetation back into landscapes, the agricultural matrix can also become both more habitable and more permeable to biodiversity, aiding in conserving biodiversity over time. By reducing the need for costly inputs while maintaining productivity, ecological intensification methods can maintain or even enhance profitability. As shown with several examples, ecological intensification and diversification can assist in creating multifunctional landscapes that are more environmentally and economically sustainable. While single methods of ecological intensification can be incorporated into large-scale industrial farms and reduce negative impacts, complete redesign of such systems using multiple methods of ecological intensification and diversification can create truly regenerative systems with strong potential to promote food production and biodiversity. However, the broad adoption of these methods will require transformative socio-economic changes because many structural barriers continue to maintain the current agrichemical model of agriculture.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it