Infaunal invertebrates and contaminants in Saint John Harbour, New Brunswick, Canada
Bibliographic record
Abstract
Drs. Heather Hunt (University of New Brunswick) and Karen Kidd (McMaster University) have been leading a long-term project sampling infaunal invertebrates, sediment contaminants, and sediment characteristics at subtidal sites in Saint John Harbour, NB, Canada. This multi-year project is establishing baseline data that can be used to assess cumulative environmental effects in Saint John Harbour. Samples were collected yearly from 2011-2013 and from 2017-2021 in Fall (October or November) at 6 reference sites (3 in the inner harbour and 3 in the outer harbour) as well as at 15 potentially impacted sites (not all potentially impacted sites were sampled in all years). Samples were collected using a 0.1 m2 Smith McIntyre grab sampler. Infaunal invertebrates were counted from a portion of the grab with a surface area of 321 cm2 and identified to species level. Concentrations of contaminants (metals and other elements; polycyclic aromatic hydrocarbons until 2018, and polychlorinated biphenyls in 2011) were measured in surface sediment (upper 5 cm). Percent organic content (loss on ignition (LOI) at 550 and 950°C), total organic carbon (calculated from LOI), percent moisture, and % composition by grain size Wentworth size classes of the sediment were determined. See Van Geest et al. (2015) and Guerin et al. (2023) for detailed field and laboratory methods. Fisheries and Oceans Canada and the Port of Saint John provided funding to collect this data. This project is part of the Coastal Environmental Baseline Program Initiative under the Oceans Protection Plan of Fisheries and Oceans Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".