Temporal variations of starch and mass in greenhouse tomato leaves under CO<sub>2</sub> enrichment
Bibliographic record
Abstract
Edwards, D., Ehret, D. and Jolliffe, P. 2011. Temporal variations of starch and mass in greenhouse tomato leaves under CO2enrichment. Can. J. Plant Sci. 91: 167–177. A plant-based method of guiding CO2 dosing may improve the effectiveness of CO2 enrichment in commercial greenhouse tomato (Lycopersicon esculentum Mill) production. The temporal dynamics of two plant response indicators, leaf starch and leaf mass per unit area (LMA) were investigated in commercial and research greenhouses throughout day/night periods, as well as after the onset of CO2 enrichment. Both leaf starch and LMA tended to follow the diurnal profile of light but with 3 to 4 h of lag. The magnitude of the response, especially for starch, was affected by leaf position, CO2 enrichment and light. The highest starch contents were measured between 1400 and 1600 and the lowest levels occurred in the morning between sunrise and 1100. In many cases plants carried over substantial starch in upper leaves from one day to the next, indicating a carbon-surplus state. In the onset experiment leaf starch and LMA increased with 4 d of exposure to CO2 enrichment for mid and upper canopy leaves and continued to increase to the end of the monitoring period (7 d). Leaf starch contents and LMA are indicators of plant carbon status that show potential for guiding CO2 dosing.
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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".