Preliminary project design for insect production: part 5 – models and simulation of insect production – organism development
Bibliographic record
Abstract
The issue dealt with here is the rearing of an organism from egg to larva to pupa as is commonly done with various mealworms and black soldier flies, while organism development is the principal subject of interest. In order to obtain a uniform product at harvest time, the population should be synchronised as much as possible and this means that variability in development in the population must be reduced to the greatest possible extent. Four main sources of variability in development were identified, two of them operational and two biological in nature. These are: (1) distribution of the initial ages of the eggs; (2) distribution of incubation temperatures of the organisms; (3) inherent variability in the development rate of individual organisms; and (4) distribution in temperature sensitivity of the organisms. The combined effects of the various sources of variability on production and productivity were examined by means of simulation. This kind of exercise is useful in a number of contexts: (a) during the very early stage of project planning, as part of preliminary project design, before any formal design work is started and well before any major financial commitments are made; (b) during trouble-shooting of a low-performing process in order to upgrade its product quality; (c) to develop specifications for egg age distribution, design specifications for incubation equipment, etc.; (d) to better understand what data are required about the organism of interest so as to facilitate process design; and (e) to set objectives for organism breeding efforts. Examples are presented of how variabilities in the various factors can combine to yield organism development distributions at harvest time and how understanding the interactions between factors can help in designing a better production process.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".