Bibliographic record
Abstract
Since the dawn of agriculture there has been a need to communicate in order to optimize production and yields. Extension and Knowledge Technology and Transfer (KTT) has seen a lot of changes over the past 100 years. None of these were as great as in the introduction of the Internet and computer technology. With this rapid change in technology, KTT workers and consultants have to adapt how information is getting to producers. Though majority of farms still prefer hard copies of publications and research, we are seeing an increase in the amount of material found online. We have also seen a shift away from demo days and on farm consultations, in order to use webinars, podcasts and social media. Farmers have a been labelled as 'slow to adapt', but this is not just a generational gap anymore. Herd size, producer personalities and delivery methods all effect how the producer wants to receive information. Though the industry is good at producing information, it is important to get it into the producers hands. Until the digital space becomes the norm for all producers there is a need to view the trends in KTT and combine traditional and new age communication methods in order to reach all farmers. As technology continues to change there will be a constant need to change how we are reaching producers while continuing with the KTT methods that have worked in the past.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.177 | 0.040 |
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".