Genomic resource development of shellfish of conservation concern
Bibliographic record
Abstract
**This is a pre-peer reviewed versio of the following article: Timmins-Schiffman EB, Friedman CS, Metzger DC, White SJ, Roberts SB (2012) Genomic resource development for shellfish of conservation concern. Molecular Ecology Resources, doi: 10.1111/1755-0998.12052, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1755-0998.12052/abstract** Effective conservation of threatened species depends on the ability to assess organism physiology and population demography. In order to develop genomic resources to better understand the dynamics of two ecologically vulnerable species in the Pacific Northwest of the United States, larval transcriptomes were sequenced for the pinto abalone Haliotis kamtschatkana kamtschatkana and the Olympia oyster Ostrea lurida. Based on comparative species analysis the Ostrea lurida transcriptome (41,136 contigs) is relatively complete. These transcriptomes represent the first significant contribution to genomic resources for both species. Genes are described based on biological function with a particular attention to those associated with temperature change, oxidative stress, and immune function. In addition, transcriptome derived genetic markers are provided. Together, these resources provide valuable tools for future studies aimed at conservation of Haliotis kamtschatkana, Ostrea lurida and related species.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".