Phylogenetics of the Daphnia longispina complex in Tibetan lakes
Bibliographic record
Abstract
To understand the phylogeography of the Daphnia longispina complex (consisting of three species: Daphnia longispina, Daphnia galeata and Daphnia dentifera) in the lakes of Tibet, we amplified the mitochondria COI sequences of the Daphnia longispina complex from Tibetan lakes and compared these with sequences from GenBank (containing Daphnia longispina from Europe, Daphnia galeata from the low altitudes area of eastern China, and Daphnia dentifera from Canada).Results showed that there is significant differentiation within Daphnia longispina, Daphnia galeata and Daphnia dentifera in the lakes of Tibet.The genetic diversity within Daphnia dentifera is 0.33-2.32%,0.33-2.74%for Daphnia galeata, and 1.33-5.50%for Daphnia longispina, representing the largest among the three species.Both Maximum Likelihood and Bayes trees based on mitochondria COI sequences showed that the Daphnia longispina complex was composed of three obvious clades, corresponding to Daphnia longispina, Daphnia galeata and Daphnia dentifera, respectively.The genetic diversity among the clades was 9.40-16.98%,according to a Kimura 2-parameter model.Haplotype network based on the mitochondria COI sequences showed that the Daphnia longispina complex was composed of three branches, corresponding to Daphnia longispina, Daphnia galeata and Daphnia dentifera, respectively.Early Chinese records showed that Daphnia longispina was widely distributed, but in this present study, Daphnia longispina only appeared in Lake Bangongcuo, and Daphnia galeata and Daphnia dentifera were more widely distributed.Because of the difficulty in morphological identification as well as the lack of molecular data in early investigations, the early records of Daphnia longispina in China were probably confused with Daphnia galeata or Daphnia dentifera.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".