Quantifying the spatial and temporal variability of the environmentalconditions in a cultivation room for the micropropagation ofcannabis
Bibliographic record
Abstract
Controlled environment agriculture (CEA) is an indoor food production technique where growth conditions are carefully monitored and controlled to optimize crop production. With the world's population and climate change continuing to rise, CEA has become increasingly important. However, many CEA facilities struggle to achieve economic sustainability due to high operating costs. One way to improve the efficiency of CEA is to better understand the variability of environmental conditions in growth enclosures. By analyzing data on temperature, humidity, CO2, and light levels in CEA environments, researchers can develop more accurate energy models and implement energy-saving measures. This is especially important for the micropropagation of cannabis, which requires precise control of environmental conditions to ensure successful plant growth. Thus, this study aims to quantify the spatial and temporal variability of environmental conditions in a cultivation room used for the micropropagation of cannabis, and to understand the impact of this variability on the growth of stage-two cannabis plantlets. To monitor the environmental conditions in the cultivation room, a low-cost Internet of Things (IoT) sensor system using Arduino technology and InfluxDB software was developed. The system includes sensors that measure temperature, humidity, CO2, and light levels, and sends the data to a web server. The study tested five different locations within the shelved cultivation room for periods of one to three weeks. Repeated measure analysis of variance (RM-ANOVA) and time series analysis were used to assess variability in environmental conditions and plantlet growth. RM-ANOVA allowed to detect a statistically significant effect of Time (pGG-corr= 0.018 < 0.05), while no statistically significant effect of Location (pGG-corr=0.092 > 0.05) or Time-Location interaction (pGG-corr=0.092 > 0.05) were detected. Time series analysis was used to gain more insights into the trends present in the data, and possible clusters in the time series related to dissimilarities in value and profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".