Effects of day and night air temperature in early season on growth, productivity and energy use of spring tomato
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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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".