Bibliographic record
Abstract
The cosmologist Stephen Hawking hasdubbed the twenty-first century the centuryof complexity. Biogeography has experienceda significant growth spurt in the past yearincorporating evolutionary complexity to adegree previously only imagined. Most of thisgrowth has been in the area of historical bio-geography, that is, studies encompassing thegeographic context of evolution. The princi-ples and theories, and methods of analysis,were not developed by molecular biologistsand are not constrained to molecular biology.Molecular data, however, add an essentialempirical dimension to this fascinating area ofresearch.As in previous recent years, the greatestnumber of publications and the greatestamount of effort by molecular biologists inbiogeography has been in the area called phy-logeography (see review by Riddle andHafner, 2004). Riddle (2005) discussed threeareas of research that are of especial interestto phylogeographers at the moment. The firstof these involves the ongoing controversyover the utility of mitochondrial DNA(mtDNA) in evolutionary studies. A growingnumber of systematists question the utility ofmtDNA for phylogeny reconstruction. Boththe high rate of evolutionary turnover andmaternal-only pattern of inheritance mayproduce incongruence between mtDNAgene trees and species phylogenies; for an excellent discussion, see Taggart
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".