Bibliographic record
Abstract
A phylogenetic tree captures the evolutionary relationships among sampled taxa – major taxonomic groups, species, infraspecific taxa, or isolates. Phylogenetic analysis is a central component of evolutionary and ecological studies, as it lends a unifying framework to draw inferences about evolutionary and ecological processes that form biodiversity. Via phylogenetic comparative methods, trait data (for example, morphological or physiological data) and geographical data may be analyzed jointly with a given phylogeny to test specific hypotheses about the evolution and ecology of focal groups of organisms. In this thesis dissertation, I present four studies demonstrating how phylogenetic analysis can yield new evolutionary and ecological insights. In the first two studies, I compare the evolutionary fates of polyploid versus diploid lineages in fish and to test whether polyploidization coincides with speciation events in land plants. Polyploid species arise from whole genome duplication and often exhibit morphological, physiological, and ecological differentiation from their diploid parents. Understanding their evolutionary patterns in the background of diploid species help us to understand why polyploidization is abundant in some organisms (plants) but not in others (fishes). In the other two studies, I explore the biodiversity of freshwater red algae in the wild and aquarium shops, using phylogenetic analyses to reveal potential introductions of these organisms via the global aquarium trade. Furthermore, I identify candidate genetic markers that may be more suitable than commonly used markers to facilitate future studies of phylogenetic community ecology of the red algae. Not only do these studies illustrate the utility of phylogenetic analyses to tackle diverse questions in evolution and ecology, but they also have forwarded the discussion on those four distinct topics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".