Preserving the agricultural data story at the Ontario Agricultural College
Bibliographic record
Abstract
Established in 1874, the Ontario Agricultural College (OAC) at the University of Guelph maintains a broad base of research activities, ranging from social and environmental conditions of rural communities, to health benefits of new foods, to more traditional plant and animal based agricultural research. Increasingly, OAC is recognized as the "research powerhouse" at the university, supported by the Ontario Ministry of Agriculture, Food and Rural Affairs and the three major federal funding agencies: Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), and Social Sciences and Humanities Research Council (SSHRC). With a broad spectrum of research, come a variety of research protocols, data formats, and outputs. Labs and research institutes develop their own data management processes, many without a preservation and access plan. To provide a cohesive approach to managing research projects, we developed a series of research data management (RDM) workshops for the OAC community. This series was piloted during the summer of 2017, and, based on its popularity, was offered again in fall 2017 and winter 2018. This presentation will discuss our tailored approach to teaching RDM along with impressions and feedback from participants. Connecting to the community through RDM is leading to hidden troves of historical agricultural data, allowing us to preserve the data story of OAC for almost 150 years.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".