Daphnia as an emerging model for toxicological genomics
Bibliographic record
Abstract
Daphnia are already an established model species in toxicology. This freshwater crustacean is used commonly for environmental monitoring of pollutants around the globe and plays an important role in establishing regulatory criteria by government agencies (e.g., US EPA, Environment Canada organization for Economic Cooperation and Development, Environment Agency of Japan). Consequently, daphniids represent 8% of all experimental data for aquatic animals within the toxicological databases (Denslow et al., 2007). As such, their incorporation within the new field of toxicological genomics is limited only by the advancement of genomic resources. Because the development of these technologies requires the input and feedback of a large research community that extends far beyond the boundaries of any one discipline, the Daphnia Genomics Consortium (DGC) was formed in 2001 to: (i) provide the organizational framework to coordinate efforts at developing the Daphnia genomic toolbox; (ii) facilitate collaborative research and (iii) develop bioinformatics strategies for organizing the rapidly growing database. This chapter reviews the progress in establishing Daphnia as model species for genomic studies, with emphasis on toxicological applications. As the goals of the DGC are defined largely by extending the boundaries of current biological research in light of genomic information, this chapter first reviews Daphnia’s unique biological attributes that make it ideal for such an expansion of research efforts. These attributes include a long tradition of ecological, evolutionary and toxicological study, culminating in the benefits provided by emerging genomic tools.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".