Development of Ex Vivo Model to Study the Effect of Rock Snot, <em>Didymosphenia geminata</em> (Lyngb.), on Cells and Gametes of Freshwater Fish
Bibliographic record
Abstract
Rock snot, a species of diatom that produces nuisance growths in international freshwater rivers and streams, in Chile, Canada, New Zealand and other country, with consistently cold-water temperatures and low-nutrient levels, is produced by Didymosphenia geminata (Lyngb.) M. Schmidt (D. geminata) and is a complex problem in rivers. Its biology problem and its effects on ecosystems are still being investigated, yet no research has focused on the effects of D. geminata at the cellular level. We proposed to review and described an artificial river system to preserve D. geminata for obtained future study material to evaluate the effects of water contaminated by this diatom on cell models. Our preliminary results indicate the D. geminata altered several cell lines and cell function, and review of the literature suggested alteration in the biotic system on river with this plague. We proposed the described literature for exploring the possibility of building a model for maintaining D. geminata from Chilean rivers and used the biological material obtained from artificial river, like extract and contaminated water for studying the chronic effects on cells.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".