GEOREFERENCED TREES AND THE PHYLOGENETIC SIMILARITY OF BIOLOGICAL COMMUNITIES
Bibliographic record
Abstract
Culture-independent DNA sequencing is being used to recover genetic material directly from environmental samples. This has spurred large-scale community efforts to catalogue the diversity of life and its geographic distribution using molecular data. These initiatives stand to revolutionize our understanding of the processes that shape biodiversity and may ultimately provide critical information for setting public health, environmental, and economic policies. To achieve these aims new tools are required to effectively explore these large biogeographic datasets. \nThis thesis introduces a novel technique for visualizing hierarchically organized data in a geographic context that illustrates the influence of a geographic or environmental gradient on the phylogenetic relationships between organisms or the similarity of biological communities. This technique is incorporated into GenGIS, open-source software that supports the integration of digital map data with genetic sequences and environmental information from multiple sample sites. GenGIS addresses the need for an interactive geospatial analysis environment capable of handling large biogeographic datasets where a wealth of sequence data is available for each sample site. This is accomplished through a rich set of analysis options that produce georeferenced visualizations for data exploration and hypothesis generation. Studies conducted by myself and other research groups have used GenGIS to investigate the diversity of viruses, bacteria, plants, animals, and even language families.\nI then explore measures of beta diversity that aim to assess the influence of geographic or environmental gradients on the similarity of biological communities. This thesis examines phylogenetic beta-diversity measures that determine community variation by considering the relationships between organisms in a phylogenetic tree. A large comparative study is performed in order to assess specific properties and performance characteristics of these measures. Many measures of phylogenetic beta diversity were found to be robust to sequence clustering, the addition of an outlying basal lineage, root placement, and the presence of rare organisms. Additionally, performance was found to differ substantially under different models of community variation. This thesis then describes how an important class of phylogenetic beta-diversity measures can be calculated over phylogenetic networks in order to account for uncertainty and conflict in inferred ancestral relationships.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".