Bibliographic record
Abstract
1 More than 200 known species of Microturbellaria occur in running waters world-wide but discovery of many more is likely. Their population density varies greatly as a function of substratum, productivity, phenology and hydrology. The density may exceed 7 000 individuals m-2. The number of species in a single small sample may reach 20. 2 Many species appear to have microhabitat or stream section specialisation but community patterns are obscured to a certain extent by common and eurytopic species. The specialisation is particularly evident in the smaller, lower-order streams. 3 Some of this habitat specialisation is attributable to the ecological origin of species that may include terrestrial, underground, marine and lentic species pools. 4 Feeding habits of Microturbellaria range from omnivory to specialised predation. 5 Quantitative field studies require extraction and examination of live specimens from samples. Such samples pose transportation and storage problems and must be processed within hours of collection. 6 Taxonomy is well resolved for the Northern Hemisphere but is likely to be a major challenge in other parts of the world. In any region, however, new species may demand caution while using current keys to their identification.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".