Bibliographic record
Abstract
The emergence of genomics tools for the evolutionary and comparative biology community led to a rapid explosion in the number of online resources targeted at this specialized community, including Web-based comparative genomics software, such as the Artemis Comparison Tool (WebACT); databases, such as PaleoDB, Global Biodiversity Information Facility, and TreeBase; and knowledge frameworks, such as the Evolution Ontology. Unfortunately, these providers are largely independent of one another and therefore the individual resources do not share any centralized plan for how the data or tools would or should be provided. As a result, there are a myriad of often incompatible technologies and frameworks being used by this community of providers. In this chapter, we explore approaches to online resource publication, both those already in use by the community, as well as new and emergent frameworks and standards. Exploration of the strengths and weaknesses of each approach, together with a brief exploration of the philosophy or informatics theory behind the varying approaches, will hopefully help readers as they navigate this data space. The discussion is constructed such that it lays the groundwork for exploration of a new global standard for data and knowledge representation--"The Semantic Web"--that holds promise of providing solutions to many of the complexities users face in their attempts to discover and integrate biodiversity data, and examples are provided.
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 | 0.085 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.031 | 0.032 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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".