Long‐read sequencing in ecology and evolution: Understanding how complex genetic and epigenetic variants shape biodiversity
Bibliographic record
Abstract
Ten years ago, the journal Molecular Ecology published a “road map” paper that reviewed past achievements in the discipline of molecular ecology, identified research challenges and charted a way forward (Andrew et al., 2013). That paper was motivated by a symposium organized during the First Joint Congress on Evolutionary Biology (Ottawa, July 6–10, 2012). In addition, it occurred on the heels of a major inflection point in molecular ecology and in life sciences more broadly: the development and uptake of “next”- or “second”-generation sequencing technologies, which deliver short DNA reads (typically shorter than 400 bp) at very high throughput (e.g., several billion reads per run; Goodwin et al., 2016). As such, Andrew et al. (2013) emphasized the promise of second-generation sequencing for diverse subdisciplines of molecular ecology such as phylogeography, landscape genomics, molecular adaptation and speciation. Representing more than just a technical advancement, second-generation sequencing was predicted to stimulate rapid conceptual breakthroughs in the field, especially in nonmodel species (Stapley et al., 2010; Tautz et al., 2010). As illustrated by any recent issue in the Molecular Ecology journal, these predictions were accurate.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".