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Record W4303644858 · doi:10.3390/foods11193037

Entomoculture: A Preliminary Techno-Economic Assessment

2022· article· en· W4303644858 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFoods · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Utilization and Effects
Canadian institutionsMcMaster University
FundersU.S. Department of Agriculture
KeywordsBiotechnologyProduction (economics)Biochemical engineeringPopulationBusinessLivestockNatural resource economicsBiologyEconomicsEngineeringEcology

Abstract

fetched live from OpenAlex

Cultured meat, or the practice of growing meat from cell culture, has been experiencing rapid advances in research and technology as the field of biotechnology attempts to answer the call to fight climate change and feed a growing global population. A major hurdle for cell-based meat products entering the market in the near-future is their price. The complex production facilities required to make such products will require advanced bioreactor systems, resources such as energy and water, and a skilled labor force, among other factors. The use of insect cells in this process is hypothesized to address some of these costs due to the characteristics that make them more resilient in cell culture when compared to traditional livestock-derived cells. To address the potential for cost savings by utilizing insect cells in the cultivation of protein-enriched foods, here we utilized a techno-economic assessment model. Three different insect cell lines were used in the model. The results indicate that insect cell lines offer potential to significantly reduce the cost per kilogram of cell cultivated meat, along with further opportunities to optimize production processes through technological advances and scaling.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.010
GPT teacher head0.230
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it