18S rRNA amplicon sequence data (V1–V3) of the Bronx river estuary, New York
Bibliographic record
Abstract
Characterising and monitoring biological diversity to foster sustainable ecosystems is highly recommended as urban centres rapidly expand. However, much of New York City’s biodiversity remains undescribed, including in the historically degraded, but recovering Bronx River Estuary. In a pilot study to identify organisms and characterise biodiversity patterns there, 18S rRNA gene amplicons (V1–V3 region), obtained from river sediments and surface waters of Hunts Point Riverside and Soundview Parks, were sequenced. Across 48 environmental samples collected over three seasons in 2015 and 2016, following quality control and contaminant removal, 2,763 Amplicon Sequence Variants (ASVs) were identified from 1,918,463 sequences. Rarefaction analysis showed sufficient sampling depth, and community composition varied over time and by substrate at the study sites over the sampling period. Protists, plants, fungi and animals, including organisms of management concern, such as Eastern oysters (Crassostrea virginica), wildlife pathogens and groups related to Harmful Algal Blooms, were detected. The most common taxa identified in river sediments were annelid worms, nematodes and diatoms. In the water column, the most commonly observed organisms were diatoms, algae of the phylum Cryptophyceae, ciliates and dinoflagellates. The presented dataset demonstrates the reach of 18S rRNA metabarcoding for characterising biodiversity in an urban estuary.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".