A Review of Long-Term Organic Comparison Trials in the U.S.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Long-term organic farming system trials were established across the U.S. to capture baseline agronomic, economic and environmental data related to organic conversion under varying climatic conditions. These sites have proven useful in providing supporting evidence for successful transition from conventional to organic practices. All experiments chosen for this review were transdisciplinary in nature; analyzed comprehensive system components (productivity, soil health, pest status, and economics); and contained all crops within each rotation and cropping system each year to ensure the most robust analysis. In addition to yield comparisons, necessary for determining the viability of organic operations, ecosystem services, such as soil carbon capture, nutrient cycling, pest suppression, and water quality enhancement, have been documented for organic systems in these trials. Outcomes from these long-term trials have been critical in elucidating factors underlying less than optimal yields in organic systems, which typically involved inadequate weed management and insufficient soil fertility at certain sites. Finally, these experiments serve as valuable demonstrations of the economic viability of organic systems for farmers and policymakers interested in viewing farm-scale organic operations and crop performance.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it