Bibliographic record
Abstract
Data associated with a DNA metabarcoding study of fish (invasive and native) biodiversity in four rivers in Ontario Sub directories 00_sample_map: Map of sampling locations and abundance of larval fishes collected at each site 01_database_build: Reference genetic database and taxonomic information for classification of metabarcoding sequencing reads 02_qiime2_analysis: De-noising and dereplicating DNA metabarcoding data 03_read_correction: Correction for contamination using no-template controls and single fish samples 04_sampling_stats: General summary statistics of sequencing 05_river_richness: Summaries and analyses of species richness across rivers 06_gear_richness: Comparison of gear types on species detected 07_primer_consistency: Comparison of primer target gene on species detected 08_sampling_depth: Comparison of number of samples collected to read depth per sample 09_database_comparison: Comparison of the reference databases for COI and 12S 10_amplicon_distance: Comparison of the genetic distance for COI and 12S metabarcoding primers
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.037 |
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".