Short‐term dynamics and long‐term recolonization of protozoa in soil
Bibliographic record
Abstract
Most studies of Protozoa in the soil are based on the “most probable number” (MPN) estimates from cultured sub‐samples. This approach has been criticized in recent years by protistologists. In order to work around these criticisms, we have tried to develop a set of procedures that rely on direct counts, without culturing. We show that the method is more sensitive and requires less effort than the MPN approach. Our species extractions focus on “active species at the time of sampling”, and we tried to estimate the variation between days, as species adjust to soil moisture and temperature changes over several days. These variations are compared to species composition and abundance fluctuations observed between seasons, in decomposing leaf litter bags. We also obtained abundance and community structure data based on samples from a 25‐year agro‐ecosystem chronosequence under no‐tillage management. We found that direct count methods, without prior culturing of samples, were more sensitive in detecting changes over several days, over several months, and in decadal succession from field samples. This provides a great advantage over culture‐based methods that generally fail to distinguish between encysted and re‐activated species.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".