Application of Non-Rectangular Hyperbola Model to the Lettuce and Beet Crops
Bibliographic record
Abstract
<div class="htmlview paragraph">Long term manned missions such as a moon base or a trip to Mars will need a life support system with a high degree of closure. Such a life support system will therefore need to be based at least partially on bio-regenerative technologies. Higher plants will very likely be part of this system due to their ability to produce food, clean water, and regenerate the atmosphere. Due to the complexity of these systems, tools for optimization and control are required. In the framework of MELiSSA [<span class="xref">1</span>], where a predictive control strategy is foreseen for the different compartments of the loop, the knowledge of a first principles based model for plant growth is mandatory.</div> <div class="htmlview paragraph">The objectives of this paper are the implementation on EcosimPro [<span class="xref">2</span>] of a first principles model for photosynthesis at leaf level [<span class="xref">3</span>], the extension to canopy level with the help of an empirical light interception model [<span class="xref">3</span>], and finally a comparison of the final canopy model to experimental results obtained in the University of Guelph from single cultures of two MELiSSA candidate crops: beet and lettuce.</div>
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 | 0.000 | 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 | 0.001 | 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".