Conservation genomics of natural and managed populations: building a conceptual and practical framework
Bibliographic record
Abstract
The boom of massive parallel sequencing (MPS) technology and its applications in conservation of natural and managed populations brings new opportunities and challenges to meet the scientific questions that can be addressed. Genomic conservation offers a wide range of approaches and analytical techniques, with their respective strengths and weaknesses that rely on several implicit assumptions. However, finding the most suitable approaches and analysis regarding our scientific question are often difficult and time-consuming. To address this gap, a recent workshop entitled 'ConGen 2015' was held at Montana University in order to bring together the knowledge accumulated in this field and to provide training in conceptual and practical aspects of data analysis applied to the field of conservation and evolutionary genomics. Here, we summarize the expertise yield by each instructor that has led us to consider the importance of keeping in mind the scientific question from sampling to management practices along with the selection of appropriate genomics tools and bioinformatics challenges.
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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 | 0.069 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".