Green chemistry and the ocean-based biorefinery
Bibliographic record
Abstract
Research into renewable chemicals, fuels and materials sourced from the oceans at Memorial University and elsewhere is employing green chemical technologies for the transformation of algae and food industry waste streams into useful products. A very small proportion of biomass utilization research is currently focused on these feedstocks and efforts focused in this area could reduce land space competition between food and chemical/fuel production. This perspective highlights some of the achievements and potential opportunities surrounding the use of algae and waste from shellfish and finfish processing. In particular, investigations in this field have used alternative solvents (water, supercritical carbon dioxide and methanol or ionic liquids) extensively. Supercritical Fluid Extraction (SFE) has been used to extract lipids and pigments from algae, and oils from fish-processing plant waste streams. Water can be used to isolate potentially high value biologically-active oligosaccharides from some seaweeds. Biotechnological approaches are showing promise in the separation of biopolymers from shellfish waste streams. Production of new nitrogen-containing bioplatform chemicals (e.g. 3-acetamido-5-acetylfuran) from aminocarbohydrates (chitin, chitosan and N-acetylglucosamine) is being pursued.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".