Application of image analysis in the fungal fermentation of <i>Trichoderma reesei</i> RUT‐C30
Bibliographic record
Abstract
Trichoderma reesei was grown in a stirred-tank bioreactor (STB) and a reciprocating plate bioreactor (RPB) at four different agitation speeds. A semiautomatic image analysis protocol that was developed to characterize the mycelium morphology during the fermentation process based on four morphological types (unbranched, branched, entangled, and clumped microorganisms) was applied to study the effect of agitation on the morphology of T. reesei. It was shown via statistical validation that broth samples used for image analysis represented the whole population of the fungi in the bioreactor. High shear was found to be damaging to T. reesei grown in the STB. The gentler shear produced in the RPB was not detrimental to the microorganism even at higher agitation speed. Better productivity was obtained for T. reesei grown in the STB and the highest productivity, 0.121 IU/mL h, was obtained at 400 rpm. The morphological parameter, the hyphal growth unit, was found to be correlated to the productivity. Understanding the effect of agitation on the morphology and productivity of T. reesei could lead to the design of better bioreactors and the selection of operating conditions of bioreactors to optimize the production of cellulase.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".