Microalgae technologies and processes for biofuels/bioenergy production in British Columbia : current technology, suitability and barriers to implementation : executive summary
Bibliographic record
Abstract
This study investigated the current state of algae technologies and research to determine the feasibility of algae cultivation in British Columbia as a bioenergy feedstock. The market analysis of energy products from algae in this study was limited to biodiesel, bioethanol and biomethane. By-products which can affect the economic potential for producing algae biomass were also considered. This report summarized the cost parameters for expected biomass yields, algae oil content, capital, labour and operational costs. There are 3 main technologies currently used to produce microalgae for bioenergy applications, notably phototrophic cultivation in open raceways; phototrophic cultivation in closed photobioreactors; and heterotrophic cultivation in closed fermenters. Although none of these processes achieve price parity with fossil fuels, the fermentation process was shown to have the lowest production cost and is considered to be the most promising method for biofuel production. It may have advantages over current start-to-ethanol pathways if algae oil can be produced with consistently high biomass productivity and oil yields. Algae harvesting is a major cost factor in bioenergy production. All three algae-based technologies reduce greenhouse gas emissions and have a positive energy balance. 204 refs., 24 tabs., 4 figs.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".