Additional file 1 of Contaminants from dredged sediments alter the transcriptome of Manila clam and induce shifts in microbiota composition
Bibliographic record
Abstract
Additional file 1: Table S1. Organic matter content and grain-size distribution in sediments collected from the six sampling sites (one replicate for each site). Table S2. Chemical analyses of metals and organic contaminants in sediments (one replicate for each site). Table S3. Bioaccumulation of metals (mg/kg dw; Table A) and organic pollutants (Table B) in clams before exposure (T0) and at the end of sediment exposure (Day 14) (one replicate for each site). Table S4. Pairwise Adonis values on Unweighted and Weighted Unifrac distances at different time-points (Table A) and among different sites for each time-point (Table B). Data collected from a total of 10 biological replicates for each site/sampling time were considered. Figure S1. Pearson’s correlation between the coordinates along the first component of variation of the sediment’s PCA and of the gene expression’s PCA. Figure S2. Principal Coordinate Analysis (PcoA) plot using Unweighted and Weighted UniFrac dissimilarities (ASV level) of the digestive gland and sediment microbiota. Data collected from a total of 10 biological replicates for each site/sampling time were considered. Figure S3. DESeq2 results by collection date. Data collected from a total of 10 biological replicates for each site/sampling time were considered. Figure S4. Alpha diversity in Manila clam microbiota and sediments. Figure S5. Barplot representing the number of unique ASVs for each bacteria Phylum in group I and group II, A and B respectively. Figure S6. Changes in relative abundance of the significant ASVs for the two groups and for every site considered: Site I (A); Site II (B,C); Site III (D,E); Site IV (F,G); Site V (H,I); Site VI (L,M). Green and Red lines identify ASVs belonging to group I and II, respectively.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.832 | 0.133 |
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".