Predicting Crop Yields and Nitrous Oxide Emissions at the Ottawa Area X.O Smart Farming Research Fields: Year Three Progress Report
Bibliographic record
Abstract
The world is facing a sharply increasing food demand that is expected to rise dramatically over the next decades. Several factors are contributing to this rise, mainly the inevitable increase in the world's population, which leads to a strong demographic pressure on the planet earth's available arable land. Moreover, an excessive use of fertilizers to maintain acceptable levels of crop yields has only exacerbated human-caused climate changes by the emission of nitrous oxide, a known potent ozone-killing greenhouse gas. To mitigate the negative effects of the population increase and nitrous oxide emissions, smart farming systems are increasingly advocated to automatically control farming input costs (such as fertilizer costs) and maximize yields while at the same time minimizing greenhouse gas emissions. Smart farming systems are cyber physical systems that automatically collect data from sensor-equipped fields, and process the collected data both locally on the fields as well as globally in the cloud. The insights gained in processing the data is used to drive farming decisions to optimize farming outputs. This paper reports on the progress made on a CyPress research project conducted on the premises of the Invest Ottawa Area X.O testing facility, where a smart farm has been set up. The project focuses on predicting crop yields and nitrous oxide emissions using data collected at the Area X.O smart farming research fields. Data gathered in these research fields are being used to develop concepts, tools and methods for engineering smart farming systems based on cyber physical 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".