Bibliographic record
Abstract
Recent progress in protistology has shown that these organisms (protists) are far more diverse than traditionally assumed by soil ecologists. Most studies have grouped these into motility groups, as amoebae, flagellates, and ciliates. Unfortunately, these do not represent functionally useful groups and do not have any ecological relevance to food web processes and community structure. Typically, abundance values have relied on the most probable number estimate based on bacterivore cultures. In fact, there are many functional groups of protists besides the bacterivores. These other functional groups are very much part of the forest soil decomposition food web, but they remain unaccounted for in models. Modelling studies have shown repeatedly that protozoan bacterivores are responsible for much of the nutrient turnover and flux through the soil food web, as they are in the aquatic microbial loop. The contribution of other protist functional groups to this nutrient cycling remains to be quantified. To this end, new sampling strategies are required, and functional diversity needs to be considered in future studies. We consider both temporal and spatial stratification as contributing factors, to explain the apparent redundancy of function. Finally, drawing on data from agricultural fields, we consider new ideas on rates of recovery after disturbance.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".