Adoption of Web-Based Spatial Tools by Agricultural Producers: Conversations with Seven Northeastern Ontario Farmers Using the GeoVisage Decision Support System
Bibliographic record
Abstract
This paper reports on the findings of a multi-site qualitative case study research project designed to document the utility and perceived usefulness of weather station and imagery data associated with the online resource GeoVisage among northeastern Ontario farmers. Interviews were conducted onsite at five participating farms (three dairy, one cash crop, and one public access fruit/vegetable) in 2014–2016, and these conversations were transcribed and returned to participants for member checking. Interview data was then entered into Atlas.ti software for the purpose of qualitative thematic analysis. Fifteen codes emerged from the data and findings center around three overarching themes: common uses of weather station data (e.g., air/soil temperature, rainfall); the use of GeoVisage Imagery data/tools (e.g., acreage calculations, remotely sensed imagery); and future recommendations for the online resource (e.g., communication, secure crop imagery, mobile access). Overall, weather station data and tools freely accessible through the GeoVisage site were viewed as representing a timely, positive, and important addition to contemporary agricultural decision-making in northeastern Ontario farming.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".