Euglena International Network (EIN): Driving euglenoid biotechnology for the benefit of a challenged world
Bibliographic record
Abstract
Euglenoids (Euglenida) are unicellular flagellates possessing exceptionally wide geographical and ecological distribution. Euglenoids combine a biotechnological potential with a unique position in the eukaryotic tree of life. In large part these microbes owe this success to diverse genetics including secondary endosymbiosis and likely additional sources of genes. Multiple euglenoid species have translational applications and show great promise in production of biofuels, nutraceuticals, bioremediation, cancer treatments and more exotically as robotics design simulators. An absence of reference genomes currently limits these applications, including development of efficient tools for identification of critical factors in regulation, growth or optimization of metabolic pathways. The Euglena International Network (EIN) seeks to provide a forum to overcome these challenges. EIN has agreed specific goals, mobilized scientists, established a clear roadmap (Grand Challenges), connected academic and industry stakeholders and is currently formulating policy and partnership principles to propel these efforts in a coordinated and efficient manner.
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 | 0.005 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".