Additional file 2 of Gut-derived metabolites influence neurodevelopmental gene expression and Wnt signaling events in a germ-free zebrafish model
Bibliographic record
Abstract
Additional file 2: Supplemental Figure 1. WMISH of notch1b in 2dpf embryos that were derived germ-free and then reintroduced to CV embryo medium. Supplemental Figure 2. Relative Abundance of bacteria in zebrafish gut sample versus zebrafish water sample at the phylum level. Taxonomy was assigned using a training set of reference sequences via the Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2. Supplemental Figure 3. Relative Abundance of bacteria in zebrafish gut sample versus zebrafish water sample at the genus level. Taxonomy was assigned using a training set of reference sequences via the Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2. Supplemental Figure 4. WMISH of axin2 in 2dpf embryos that were derived germ-free and then treated once, or twice with zebrafish metabolites. Supplemental Figure 5. Single layer composite of axonal tracks. Supplemental Figure 6. Projected images of a-tubulin and GFAP:GFP expression. 3dpf lateral line composite trunk. Supplemental Figure 7. WMISH of 4 and 5 dpf larvae with notch, ascl1a and isl1 Supplemental Figure 8. 3dpf lateral line composite. Supplemental Figure 9. 3dpf lateral line composite of posterior lateral line. Supplemental Figure 10. 4 dpf lateral line composite of posterior lateral line. Supplemental Figure 11. KEGG images of various signalling pathways.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.728 | 0.115 |
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".