Combining morphology, behaviour and genomics to understand the evolution and ecology of microbial eukaryotes
Bibliographic record
Abstract
Microbial eukaryotes (protists) are structurally, developmentally and behaviourally more complex than their prokaryotic cousins. This complexity makes it more difficult to translate genomic and metagenomic data into accurate functional inferences about systems ranging all the way from molecular and cellular levels to global ecological networks. This problem can be traced back to the advent of the cytoskeleton and endomembrane systems at the origin of eukaryotes, which endowed them with a range of complex structures and behaviours that still largely dominate how they evolve and interact within microbial communities. But unlike the diverse metabolic properties that evolved within prokaryotes, the structural and behavioural characteristics that strongly define how protists function in the environment cannot readily be inferred from genomic data, since there is generally no simple correlation between a gene and a discrete activity or function. A deeper understanding of protists at both cellular and ecological levels, therefore, requires not only high-throughput genomics but also linking such data to direct observations of natural history and cell biology. This is challenging since these observations typically require cultivation, which is lacking for most protists. Potential remedies with current technology include developing a more phylogenetically diverse range of model systems to better represent the diversity, as well as combining high-throughput, single-cell genomics with microscopic documentation of the subject cells to link sequence with structure and behaviour. This article is part of a discussion meeting issue 'Single cell ecology'.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".