The story of long-term research sites and soil health in Canadian agriculture
Bibliographic record
Abstract
Canada's interest in agricultural lands has changed with time from a desire of crop yields at Confederation through to discussions in the Senate on adaptation and resilience in 2018. Long-term research experiments (LTRs) have been present and utilized by federal and university researchers to provide answers throughout. Here we highlight the importance of LTRs by identifying the historical context of LTRs and soil health research in Canada. We then briefly describe the history and key results from select LTRs and illustrate the wealth of information collected from the North American Project to Evaluate Soil Health Measurements cross-country point-in-time soil sampling from these LTRs. We discuss the LTRs, and the knowledge gained from them, with the hope that by showing the distinctive narratives associated with each of these study sites, researchers will be inspired to use them to address their research questions and make sound predictions to facilitate the adaptation of Canadian agroecosystems to climate challenges. Through identifying the value generated by these unique LTRs, we hope that the importance of these sites will inspire not only their continued maintenance but also the next generation of LTRs.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.056 | 0.035 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".