Bibliographic record
Abstract
Drawing on Canadian and Danish data, this text deals with the question of costs and income for dairy producers interested in making the transition toward organic farming practices. These data allow for the comparison of yields, expenses and income of organic farmers with those of conventional farmers. However, before analysing the results, some qualifications are in order. (1) Inasmuch as the environmental costs of these practices have not been taken into account, full comparisons cannot be made. (2) Research in organic production has not reached the level of development as has that of its conventional counterpart. (3) Transition costs must also be considered when comparing the situations. (4) While costs may have an impact on interest in organic milk production, income can be equally important. (5) Comparisons paint a static picture of the situation as potential variation in input costs cannot be taken into account. It can be concluded from the analysis that, while not a universal panacea, organic milk production appears to be a serious option in terms of agricultural practices. It has so far allowed a still limited number of farm operations over a relatively short period of time to obtain results that are just as satisfactory as those from conventional farming. A healthy initial financial situation and good management skills on the part of farmers as well as government support and the market's willingness to pay are certainly factors which will facilitate the transition. When the medium and long-term environmental gains are also counted in, it is likely that farmers, the authorities and the population will find this option all the more appealing.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".