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
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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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 | 0.001 |
| 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".