University farm benefits from a long-term student-generated soil data set
Bibliographic record
Abstract
To enhance student learning about impacts of soil management practices, the Sustainable Soil Management course at the University of British Columbia (UBC), Vancouver led development of a long-term, student-generated soil data set focused on the UBC Farm, a teaching, research, and community engaged production farm. The objectives of this paper are to (i) describe development of the student-generated soil data set, (ii) illustrate data interpretation done by students in the Sustainable Soil Management course, and (iii) outline key implications of having the long-term student-generated data set for sustainable soil management at a university farm. The data set, generated by students using the same sampling protocol and analytical methods since 2004, provides a long-term record of soil properties for each of the 27 fields at the UBC Farm. Students are engaged in a real-life scenario, collecting data and assessing the impacts of soil management practices on soil health. Concurrently, the data set allows the farm manager to assess the impacts of their soil management practices, and to monitor soil health. Despite various challenges such as the need for continuing funding for laboratory analyses, quick turnaround time of laboratory analyses, and ongoing maintenance of the database associated with the student-generated soil data set, having such a data set are still of enormous importance, benefiting both students and farm managers. The UBC student-generated soil data set can serve as an example for other instructors interested in involving students in long-term monitoring and data generation at university farms.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".