Bibliographic record
Abstract
the same result?"For a similar site, a farmer may apply the same treatment and see the same results, but locations with a different site history, different soils, or different weather patterns are unlikely to have the same outcome as the researchers.These differences among sites are typically of interest to researchers, and understanding the mechanisms that control outcomes improves recommendations and best practices.Dr. Nicolas Tremblay, a researcher with Agriculture and Agri-Food Canada, and colleagues observed differences in outcomes when applying the same treatments at sites across North America.A meta-analysis "gave us confidence in the key parameters" responsible for the different experimental outcomes, specifically that soil texture and precipitation regulate nitrogen uptake by corn.Knowing that soil and weather data are easily obtained for most sites, Tremblay, a member of ASA, CSSA, and SSSA, and his colleagues developed a tool that uses soil and precipitation data as inputs to provide a custom recommended level of nitrogen fertilizer to corn growers.The tool is called SCAN, which stands for Soil, Crops, and Atmosphere for Nitrogen management, and it can make field-specific nitrogen recommendation
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 | 0.108 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.081 | 0.053 |
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".