The historical biogeography of co‐evolution: emerging infectious diseases are evolutionary accidents waiting to happen
Bibliographic record
Abstract
Abstract Ecological fitting refers to interspecific associations characterized by ecologically specialized, yet phylogenetically conservative, resource utilization. During periods of biotic expansion, parasites and hosts may disperse from their areas of origin. In conjunction with ecological fitting, this sets the stage for host switching without evolving novel host utilization capabilities. This is the evolutionary basis of emerging infectious diseases (EIDs). Phylogenetic analysis for comparing trees (PACT) is a method developed to delineate both general and unique historically reticulated and non‐reticulated relationships among species and geographical areas, or among parasites and their hosts. PACT is based on ‘Assumption 0’, which states that all species and all hosts in each input phylogeny must be analysed without modification, and the final analysis must be logically consistent with all input data. Assumption 0 will be violated whenever a host or area has a reticulated history with respect to its parasites or species. PACT includes a Duplication Rule, by which hosts or areas are listed for each co‐evolutionary or biogeographical event affecting them, which satisfies Assumption 0 even if there are reticulations. PACT maximizes the search for general patterns by using Ockam's Razor – duplicate only enough to satisfy Assumption 0. PACT applied to the host and geographical distributions of members of two groups of parasitic helminths infecting anthropoid primates indicates a long and continuous association with those hosts. Nonetheless, c. 30% of the host associations are due to host switching. Only one of those involves non‐primate hosts, suggesting that most were constrained by resource requirements that are phylogenetically conservative among primates (ecological fitting). In addition, most of the host switches were associated with episodes of biotic expansion, also as predicted by the ecological fitting view of EIDs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".