Bibliographic record
Abstract
A diverse array of molecular markers and constantly evolving analytical approaches have been employed to reconstruct the invasion histories of the most notorious invasions. Detailed information on the source(s) of introduction, invasion route, type of vectors, number of independent introductions and pathways of secondary spread has been corroborated for a large number of biological invasions. In this review, I present the promises and limitations of current techniques while discussing future directions. Broad phylogeographic surveys of native and introduced populations have traced back invasion routes with surprising precision. These approaches often further clarify species boundaries and reveal complex patterns of genetic relationships with noninvasive relatives. Moreover, fine-scale analyses of population genetics or genomics allow deep inferences on the colonization dynamics across invaded ranges and can reveal the extent of gene flow among populations across various geographical scales, major demographic events such as genetic bottlenecks as well as other important evolutionary events such as hybridization with native taxa, inbreeding and selective sweeps. Genetic data have been often corroborated successfully with historical, geographical and ecological data to enable a comprehensive reconstruction of the invasion process. The advent of next-generation sequencing, along with the availability of extensive databases of repository sequences generated by barcoding projects opens the opportunity to broadly monitor biodiversity, to identify early invasions and to quantify failed invasions that would otherwise remain inconspicuous to the human eye.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".