Additional file 2 of Biomonitoring 2.0 Refined: observing local change through metaphylogeography using a community-based eDNA metabarcoding monitoring network
Bibliographic record
Abstract
Additional file 2: Figures S1–S8. Fig. S1 Schematic of multiple sequence alignment filtering. Fig. S2 Schematic of ESV merging by cluster and dissimilarity matrix generation. Fig. S3 Schematic of ESV grouping by cluster and mean dissimilarity matrix generation. Fig. S4 Schematic of ESV scrambling by cluster to generate scrambled clusters. Fig. S5 Comparisons of adjusted Rand index using different numbers of cluster centers to separate sites into region groups. Fig. S6 Comparisons of total within-cluster sum of squares using different numbers of cluster centers to separate sites into region groups. Fig. S7 Intraspecific genetic variation separates region groups with dissimilarity patterns that differ from community β-diversity (MLJG). Fig. S8 Multiple sequence alignment of Yoraperla brevis ESVs and barcodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.849 | 0.198 |
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".