Macronutrient-Based Predictive Modelling of Bioconversion Efficiency in Black Soldier Fly Larvae (Hermetia illucens) Through Artificial Substrates
Bibliographic record
Abstract
This study explores the optimisation of rearing substrates for black soldier fly larvae (BSFL). First, the ideal dry matter content of substrates was determined, comparing the standard 30% dry matter (DM) with substrates hydrated to their maximum water holding capacity (WHC). Substrates at maximal WHC yielded significantly higher larval survival rates (p = 0.0006). Consequently, the WHC approach was adopted for further experiments. Using these hydrated artificial substrates, fractional factorial designs based on central composite and Box–Behnken designs were employed to assess the impact of macronutrient composition on bioconversion efficiency. The results demonstrated significant main, interaction, and quadratic effects on bioconversion efficiency. Validation with real-life substrates of varied protein content, including indigestible feather meal, affirmed the predictive model’s accuracy after accounting for protein source digestibility. This research underscores the importance of optimal hydration and macronutrient composition in enhancing BSFL growth and bioconversion efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".