Comparison of Two Harvest Methods for Lettuce Production in an Aquaponic System
Bibliographic record
Abstract
<p>Aquaponics is an integrated food production technology of aquaculture and hydroponics. Lettuce (<em>Lactuca sativa </em>L.) is an economically important vegetable crop that can be grown aquaponically. In addition to selecting the right choice of lettuce cultivars, developing an optimal harvest strategy could increase lettuce production. Lettuce production using two harvest methods, Cut-and-Come-Again (CC) and Once-and-Done (OD), was evaluated using ‘Red Sails’ lettuce in a flow-through aquaponic system rearing trout. With the CC method continual harvesting was possible on a weekly basis after the initial harvest, while it took five weeks for each harvest using the OD method. The total yield of lettuce by the CC method was 6.7 kg from 9 trays, while 22.6 kg of lettuce was harvested by the OD method using 54 trays. In harvests by the OD method, 6 times as many seeds were sown compared to the CC method. The average yield per tray harvested by the CC method (744.4 g/tray) was 78% higher than that by the OD method (418.5 g/tray) because the CC method used 6 times less trays. Productivity, calculated by the average yield per growing week, of the two harvest methods at the first harvest was similar, but 4.8 times higher in the CC method than in the OD method at the second harvest due to the shorter harvest time. However, visual and decay ratings of lettuce harvested by the CC method began to decline afterwards. Together, the OD method after two consecutive harvests by the CC method would help growers to obtain increased yield of quality lettuce.</p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".