Effects of day and night air temperature in early season on growth, productivity and energy use of spring tomato
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
Effects of air temperature on tomato (Lycopersicon esculentum Mill) growth, yield and heating energy consumption were investigated in spring of 1993 and 1994. Tomato plants were grown under nine day/night air temperature regimes formed by factorial combination of three day (19, 20 and 21°C) and three night (16, 17 and 18°C) heating temperature set points. Early (until 30 April) fruit yield increased but early fruit size decreased with increasing daily average air temperature (MT, 24-h mean). The plants grown under high daily average air temperature early in the season had lower fruit yield late in the season. Plants grown under high night air temperature (NT) and low day air temperature (DT) during the early production period achieved high fruit yield in early season and avoided the negative effects of high MT on early fruit size; these plants also had high yield and large fruit size late in the season. The different day and night heating temperature regimes studied caused no more than 10% in heating energy use variation. Therefore, for greenhouse tomato production under Great Lakes conditions (approx. 42°N), the optimal day/night air temperature (from January to April) is 20.8–21.0/18.5–19.0°C (actual air temperatures). Key words: Lycopersicon esculentum, tomato, yield, quality, fruit size, daily average air temperature (MT), day-night air temperature difference (DIF), day air temperature (DT), night air temperature (NT)
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it