Regenerative, Semiclosed Systems: A Priority for Twenty-First-Century Agriculture
Bibliographic record
Abstract
This overview draws attention to several reasons to encourage the design of new agronomic systems, shifting from conventional open or leaky systems to more closed, regenerative systems: Current systems cause overconsumption of environmental resources, contribute to climate change, rely on increasingly expensive fossil fuel, and result in environmental (e.g., groundwater) contamination. Moreover, the agronomic–urban interface is growing, as are markets for ecologically friendly produce, the need for low-input farming systems in low-income regions, and disenchantment with the subsidization of conventional agriculture. There is reasonable biological and economic evidence to support advocacy for a shift to regenerative systems. Such a shift presents challenges—for example, although higher labor input enhances community well-being and rural social capital, it is costly. It also offers opportunities—for example, to adapt technologies to monitor and minimize wastage. Shifting to semiclosed systems would be accelerated by (a) routine life cycle analysis and costing; (b) calculation of the full costs to society of farm inputs such as pesticides; (c) food labeling and standards that draw attention to energy and other inputs; (d) government grants supporting the transition to semiclosed systems; (e) changing priorities for agronomic research; and (f) greater engagement of urban societies in agriculture through recreation and philanthropy.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".