Effects of Calcium and Magnesium on Plant Growth, Biomass Partitioning, and Fruit Yield of Winter Greenhouse Tomato
Bibliographic record
Abstract
Tomato ( Lycopersicon esculentum Mill) `Trust' was grown on rockwool with nutrient solutions containing two levels of calcium (150 and 300 mg·L -1 ) in factorial combination with three levels of magnesium (20, 50, and 80 mg·L -1 ) in Winters 1997 and 1998, to investigate the effects of calcium and magnesium on growth, biomass partitioning, and fruit production. Plants grown at 20 mg·L -1 Mg started to show Mg deficiency symptoms (leaf chlorosis) at 8 weeks after planting. The chlorophyll content of middle and bottom leaves increased with increasing Mg concentration in the nutrient solution. At 300 mg·L -1 Ca, total fruit yield and fruit dry matter increased linearly with increasing Mg concentration; marketable fruit yield and total plant biomass showed similar response but to a lower degree. At 150 mg·L -1 Ca, total plant biomass, fruit dry matter and yield peaked at 50 mg·L -1 Mg. The biomass allocation to fruit increased while allocation to leaves decreased with increasing Mg concentration. The Mg effects on total and marketable fruit yield were mainly due to its influence on fruit yield in the late growth stage. Incidence of blossom-end rot (BER) at 150 mg·L -1 Ca increased linearly with increasing Mg concentration while it was not affected by Mg concentration at 300 mg·L -1 Ca. For a winter greenhouse tomato crop, the appropriate Ca and Mg concentrations for tomato production appear to be at 300 and 80 mg·L -1 , respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".