Morphological and molecular characterization of twenty-five new Diploneis species (Bacillariophyta) from Lake Tanganyika and its surrounding areas
Bibliographic record
Abstract
Lake Tanganyika, the second oldest lake in the world, is home to some of the largest and most phenotypically, genetically, and ecologically diverse freshwater assemblages. While the evolutionary processes responsible for generating its extraordinary richness are well understood across various animal groups, little is known about the diatom species richness, turnover patterns, and their drivers. Here, we explored species richness of the diatom genus Diploneis from Lake Tanganyika and surrounding regions by using morphological and molecular information. Both datasets suggested the presence of twenty-five new Diploneis species, each not related to any known Diploneis species so far. The multi-locus phylogeny indicates a monophyletic group of twenty-one Diploneis species from Lake Tanganyika, suggesting potential intralacustrine diversification possibly triggered by certain historical contingencies. This is supported by the small genetic distances and the presence of unique silica ornamentations on the valve face exterior, to date only known to occur on the Baikal endemic Diploneis implicatus. The discovery of such a high richness of Diploneis species in Lake Tanganyika and the presence of a unique morphological character points to a potential adaptive radiation - a mechanism yet to be confirmed.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".