Feeling the heat: Temperature and fertilizer's role in cooking up a high yielding raspberry crop ( <b> <i>Rubus idaeus</i> </b> ) grown in a controlled, indoor, hydroponic environment
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Bibliographic record
Abstract
Background: Temperature and fertilizer crucially influence fruit quality. While well-studied for outdoor-grown red raspberries, optimal conditions for controlled indoor agriculture are less understood. Objectives: This study aimed to identify the best temperature and fertilizer regimen to maximize fruit production, sweetness, and harvest index in an indoor, hydroponic vertical farm. Methods: We tested three temperatures (21, 23, 25°C) and three fertilizer mixes (A: weak fertilizer applied at a constant rate, B: developmentally adjusted fertilizer (DAF) and C: DAF plus commercial endomycorrhizal fungi) on 'Joan J' raspberries in a controlled indoor hydroponic vertical farm in Toronto, Canada. We measured fruit number, weight, and sugar content. Results: Raspberries grown at 23°C produced significantly more (∼30%) total fruit biomass than those at 21 and 25°C (F = 17.19, P<0.001). Fruit weight was higher earlier in the season, decreasing by 29% in the following three months. Temperature and time interacted such that the largest fruit was produced at 21°C in the first month (F = 3.70, P < 0.001). Fertilizer B yielded significantly greater (26-35%) more fruit and harvest index than Fertilizers A or C (F=5.16, P<0.001), though no significant differences were found in the interaction between fertilizer and time. Additionally, raspberries grown at 23°C had significantly higher sugar content (9.89°Bx, P < 0.05) compared to other temperatures, but fertilizer did not influence sweetness. Conclusions: While 21°C yielded the most fruit early in the season, 23°C produced the highest overall yield and sweetest fruit, lower than typical outdoor conditions for temperate climate raspberries. Developmentally adjusted fertilizers increase raspberry yield.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it