Eukaryotic systematics: a user's guide for cell biologists and parasitologists
Bibliographic record
Abstract
Single-celled parasites like Entamoeba, Trypanosoma, Phytophthora and Plasmodium wreak untold havoc on human habitat and health. Understanding the position of the various protistan pathogens in the larger context of eukaryotic diversity informs our study of how these parasites operate on a cellular level, as well as how they have evolved. Here, we review the literature that has brought our understanding of eukaryotic relationships from an idea of parasites as primitive cells to a crystallized view of diversity that encompasses 6 major divisions, or supergroups, of eukaryotes. We provide an updated taxonomic scheme (for 2011), based on extensive genomic, ultrastructural and phylogenetic evidence, with three differing levels of taxonomic detail for ease of referencing and accessibility (see supplementary material at Cambridge Journals On-line). Two of the most pressing issues in cellular evolution, the root of the eukaryotic tree and the evolution of photosynthesis in complex algae, are also discussed along with ideas about what the new generation of genome sequencing technologies may contribute to the field of eukaryotic systematics. We hope that, armed with this user's guide, cell biologists and parasitologists will be encouraged about taking an increasingly evolutionary point of view in the battle against parasites representing real dangers to our livelihoods and lives.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.081 | 0.146 |
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".