Bibliographic record
Abstract
The project aim is to help the participants to a leading position in the field of utilizing algae for energy purposes and for commercial exploitation of high value compounds from algae. An additional aim is to increase the synergy and facilitating collaboration between the participants involved in the project and thereby increase their ability to compete in this new field. Algae are the largest un-exploited biomass resource, which possess vast potential as resource for an array of different applications includingsustainable energy carriers, chemicals, pharmaceuticals and ingredients for the food and feed industry. Industrial scale utilization of marine algae requires intensive development ofgrowth, harvest and conditioning systems that secure reliable delivery of large amount of biomass at the right time, quality and condition. A long innovative process is necessary tobe able to scale up the algae production to meet the increasing demand for biomass for many different purposes. This project is a network project with focus on a majority of industrial partners in dialogue with research institutions. The network will increase theability of the involved industries to evaluate their business opportunities for production based on algae raw materials, and the network will strengthen the cooperation andsparring between the Nordic partners. In addition the newsletters and the website will give a large network for algae activities in the North Atlantic area including England, Scotland.Ireland, Faroe Island, Greenland and the east coast of Canada.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.197 | 0.114 |
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".