Isolation, Preliminary Characterization and Preliminary Assessment of Scale-Up Potential of Photosynthetic Microalgae for the Production of Both Biofuels and Bio-Active Molecules in the U.S. and Canada (CRADA Final Report)
Bibliographic record
Abstract
Combustion flue gases are a major contributor to carbon dioxide emissions into the Earth's atmosphere, a factor that has been linked to the possible global climate change. It is, therefore, critical to begin thinking seriously about ways to reduce this influx into the atmosphere. Using carbon dioxide from fossil fuel combustion as a feedstock for the growth, photosynthetic microorganisms canprovide a large sink for carbon assimilation as well as a feedstock for the production of significant levels of biofuels. Combining microalgal farming with fossil fuel energy production has great potential to diminish carbon dioxide releases into the atmosphere, as well as contribute to the production of biofuels (e.g., biodiesel, renewable diesel and gasoline and jet fuel) as well as valuableco-products such as animal feeds and green chemicals. CO2 capture may be a regulatory requirement in future new coal or natural gas power plants and will almost certainly become an opportunity for commerce, the results of such studies may provide industries in the US and Canada with both regulatory relief and business opportunities as well as the ability to meet environmental and regulatoryrequirements, and to produce large volumes of fuels and co-products.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".