Pleurogene: genomics for the enhancement of commercial production of Atlantic halibut and Senegal sole
Bibliographic record
Abstract
Atlantic halibut (Hippoglossus hippoglossus) and Senegal sole (Solea senegalensis) are two flatfishes yielding high value market products with good potential for aquaculture in eastern North America and Mediterranean Europe, respectively. Production–related problems in these two evolutionary–related species may be addressed with improved knowledge of important basic biological processes such as reproduction, development, nutrition, genetics and immunity. The use of genomic approaches to thoroughly characterize these processes will translate into knowledge that can be used to overcome the production obstacles and create (for PLEUROGENE is a new initiative funded through Genome Canada-Genome Spain for three years (2004-2007). There are two main goals: (i) the construction of genetic linkage maps Atlantic halibut and Senegal sole for use in the selection of improved broodstock based on molecular markers, and (ii) design, construction a flatfish microarray studies gene expression these two species. High-throughput genome- proteome-based technologies will be used identification, characterization mapping genes important reproduction, larval development, immunity nutrition. All the genetic and molecular information obtained in this project will be integrated into an interactive bioinformatic platform specifically developed for the project. The knowledge generated by the PLEUROGENE project will ultimately lead to the establishment of new technologies for the control of reproduction and optimization of larval health and nutrition in the Senegal sole, Atlantic halibut, and other related flatfish species under intensive culture conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".