Bibliographic record
Abstract
In Canada, the control of relative humidity is a key issue in greenhouse production as it has a direct and significant effect on dehumidification cost, crop quality and yield. Experiments were carried out to measure plant transpiration rate and fruit yield under four different ambient water vapour pressure deficits. Four identical greenhouses were used to produce tomatoes (Lycopersicon esculentum Mill.) under four different regimes of water vapour pressure deficit (VPD). Dehumidification costs were highly correlated to VPD: low VPD produced low transpiration requiring little dehumidification. Thus, managing plant transpiration can lead to a more efficient use of transpiration for crop production. A model was developed to optimise greenhouse climatic conditions to maximize net profit. The present project validated this model, and compared measured values with those calculated from the transpiration and condensation sub-models and from the entire model itself. The sub-models and entire model proved to be accurate within 3% when used to simulate ideal climatic conditions for periods of one week or longer. Model sensitivity was greatest for exterior temperature because this factor affects heating costs without increasing yields. Using winter climatic conditions typical of Quebec City, Canada, three greenhouse climate control strategies were simulated and compared with respect to energy consumption and yield of a tomato crop. The merit and drawback of each strategy are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".