Year-Round Lettuce (Lactuca sativa L.) Production in a Flow-Through Aquaponic System
Bibliographic record
Abstract
<em><em></em></em><p>Aquaponics is the combination of hydroponics and aquaculture that sustainably produces both animal and plant food products. Soluble nutrients are released into water by the fish providing nutrition for plant growth. Lettuce (<em>Lactuca sativa</em> L.) is one of the most popular vegetables grown in aquaponic systems. In this experiment, the feasibility of year-round lettuce production utilizing a cold water flow-through aquaponic system (FTS) growing trout (<em>Oncorhynchus mykiss</em>) in a high tunnel was evaluated. A high tunnel is a greenhouse-like facility constructed with polyethylene covering a metal frame which extends the growing season and protects the crop from cold temperatures. The average night air temperature inside the high tunnel during winter in Wardensville, WV was 2.9±3.4 °C and it helped extend the growing period into the fall and winter. Results from this pilot scale experiment showed the potential for year-round lettuce production in an FTS. Average yield (fresh harvest weight per tray) in the spring season was the highest, while productivity (average yield per week) during the summer season was higher than that in spring. During the extended growing seasons (fall and winter), more than a quarter (30.6%) of the total lettuce production was obtained. The yield per unit area (7.4 kg m<sup>-2</sup>) from our pilot study was significantly higher than that from the reported average field production (3.1 kg m<sup>-2</sup>) in the U.S. except California and Arizona where year-round production of lettuce occurs. To compensate for lower lettuce yields during cold seasons, high value crops requiring less nutrients and tolerant to the colder environment may be considered.</p>
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".