Euglena International Network (EIN): Driving euglenoids into the biotechnology world
Bibliographic record
Abstract
Abstract teaserEuglenoids show great promise to benefit our world; as biofuels, environmental remediators, anti-cancer agents, robotics design simulators and food nutritional agents, but the absence of reference genomes currently limit realizing these benefits. The Euglena International Network (EIN) (https://euglenanetwork.org/) aims to address these challenges, and is currently seeking formative phase support and funding.Body startOf the nearly 1000 known species of euglenoids (Triemer and Zakryś, 2015), including Euglena gracilis and Rhabdomonas costata, fewer than 2 % have been explored for any level of translational potential in the past 20 years. The absence of reference genomes currently limits biotechnology applications, including the development of efficient tools for genetic manipulation in euglenoids.EIN aims to advance euglenoid science through a creative amalgam of academic institutions, national research institutes and biotechnology industry, to translate and exploit euglenoids through genome sequencing. EIN has defined goals, mobilized scientists, established a clear roadmap (Grand Challenges), connected academic and industry professionals and is currently formulating policy and partnership principles, driven by EIN Executive and Science committees. However, for EIN’s activities to be maintained and durable, long-term support is vital. We call on national and continental funding agencies and research councils, protists and algae communities, and biotechnology and pharmaceutical industries, to embrace, support and fund translational exploitation of these highly valuable organisms.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".