A Resurgence in Field Research is Essential to Better Understand the Diversity, Ecology, and Evolution of Microbial Eukaryotes
Bibliographic record
Abstract
The discovery and characterization of protist communities from diverse environments are crucial for understanding the overall evolutionary history of life on earth. However, major questions about the diversity, ecology, and evolutionary history of protists remain unanswered, notably because data obtained from natural protist communities, especially of heterotrophic species, remain limited. In this review, we discuss the challenges associated with "field protistology", defined here as the exploration, characterization, and interpretation of microbial eukaryotic diversity within the context of natural environments or field experiments, and provide suggestions to help fill this important gap in knowledge. We also argue that increased efforts in field studies that combine molecular and microscopical methods offer the most promising path toward (1) the discovery of new lineages that expand the tree of eukaryotes; (2) the recognition of novel evolutionary patterns and processes; (3) the untangling of ecological interactions and functions, and their roles in larger ecosystem processes; and (4) the evaluation of protist adaptations to a changing climate.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".