<i>(Invited)</i> Green Ammonia Applications: Opportunities and Challenges for on-Farm Adoption
Bibliographic record
Abstract
Renewable-based ammonia production (hereafter, green ammonia) could present a transformative opportunity for agricultural systems, offering a pathway to decentralize and decarbonize fertilizer production. Modular green ammonia units that can be deployed on-farm are emerging around the world, with individual annual production capacities of 100 to 500 tonnes. Decentralized production is poised to increase ammonia availability and fertilizer accessibility, while decarbonizing production, lowering transport emissions, and enhancing farm resilience to supply chain disruptions. However, high capital and operating costs for modular green ammonia units, as well as access to water and renewable energy, pose significant adoption barriers for farmers. Safety concerns and mismatches between typical green ammonia products ( e.g., anhydrous ammonia) and existing fertilizer practices further complicate integration into the agricultural sector. Critically, widespread green ammonia availability could also risk fertilizer overuse, undermining environmental benefits associated with decarbonized ammonia production. This talk will explore opportunities, challenges and critical considerations for integrating green ammonia into agricultural systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.023 |
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".