Season, sowing date, and row cover influences the production of cool season vegetables in movable high tunnels
Bibliographic record
Abstract
Moveable high tunnels offer the possibility of increasing the number of crops harvested from a given piece of ground in northern latitudes where there is a short growing season. In an effort to expand crop scheduling options, three leafy greens and three root vegetables were grown in the spring in a movable high tunnel, and in the fall were sown outside and the tunnel was moved over the crops in late September. The effects of seeding date and addition of row cover were further explored on fresh weight and days to harvest. Using row cover within the high tunnel increased growing degree hours (GDH) by an average of 29% in the spring and 17% in the fall over a high tunnel without row cover. Soil degree hours (SDH) in the high tunnel with row cover increased an average of 9% in the spring and 12% in the fall over the high tunnel without row cover. The addition of row cover increased yield of leafy greens and turnip by an average of 35% in spring 2018 when the outside air temperature was considerably below average. Early-seeded fall leafy greens out-yielded late-seeded by 52% due to the ability to make a second harvest. Using row cover within the high tunnel increased GDH and SDH during both spring and fall seasons and increased the yield of cool season vegetables when outside air temperatures were considerably below average.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".