Applying functional genomics to the study of lamprey development and sea lamprey population control
Bibliographic record
Abstract
Lampreys are one of the few survivors of an ancient lineage of jawless vertebrates and have become an important study organism in numerous disciplines in the biological sciences, including evolutionary biology, embryology, ecology, physiology and biomedicine. At the same time, however, lampreys have created economic and ecological problems due, primarily, to the invasion of parasitic sea lamprey (Petromyzon marinus) into the North American Great Lakes and consequent negative impacts on local fish populations. Barriers, trapping and lampricide treatments have reduced these impacts, but concern for habitat restoration, non-target effects and possible evolution of resistance to lampricides suggests the need to develop additional strategies that supplement current control measures. The advent of functional genomics, and in particular CRISPR/Cas9 genome editing, offers a molecular approach to this on-going problem. Here, we review the successful application of functional genetic, transcriptomic, and CRISPR/Cas9 genome editing technologies in lampreys to address basic research questions in the fields of evolutionary and developmental biology. We then describe how these tools may be repurposed for use by fishery and conservation biologists to approach the problem of invasive sea lamprey from a molecular-genetic perspective.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".