Machine learning analyses of bacterial oligonucleotide frequencies to assess the benthic impact of aquaculture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aquaculture is a rapidly expanding industry and is now one of the primary sources of all consumed seafood. Intensive aquaculture production is associated with organic enrichment, which occurs as organic material settles onto the seafloor, creating anoxic conditions which disrupt ecological processes. Bacteria are sensitive bioindicators of organic enrichment, and supervised classifiers using features derived from 16s rRNA gene sequences have shown potential to become useful in aquaculture environmental monitoring. Current taxonomy-based approaches, however, are time intensive and built upon emergent features which cannot easily be condensed into a monitoring pipeline. Here, we used a taxonomy-free approach to examine 16s rRNA gene sequences derived from flocculent matter underneath and in proximity to hard-bottom salmon aquaculture sites in Newfoundland, Canada. Tetranucleotide frequencies (k = 4) were tabulated from sample sequences and included as features in a machine learning pipeline using the random forest algorithm to predict 4 levels of benthic disturbance; resulting classifications were compared to those obtained using a published taxonomy-based approach. Our results show that k-mer count features can effectively be used to create highly accurate predictions of benthic disturbance and can resolve intermediate changes in seafloor condition. In addition, we present a robust assessment of model performance which accounts for the effect of randomness in model creation. This work outlines a flexible framework for environmental assessments at aquaculture sites that is highly reproducible and free of taxonomy-assignment bias.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it