Assessing the viability of pre-industrial sediment prior to remediation using primary producer (<i>Zostera marina</i> and <i>Spartina alterniflora</i>) growth and survival
Bibliographic record
Abstract
Boat Harbour, Nova Scotia, Canada, has served as a polishing pond for treated pulp and paper effluent since 1967. In 2020, the effluent flow ceased, and the site will be remediated. The focus of the remediation is the removal of a layer of contaminated sediment, shown to contain high levels of metals and dioxins and furans. Our primary objective was to test whether the underlying pre-industrial sediment could support growth and survival of estuarine plants. A large-diameter (15 cm) corer was used to extract cores from underneath the contaminated layer. These cores were inserted into a reference estuary, along with cores of reference estuarine sediment. Four 3 × 3 subtidal plots were used to test transplants of the estuarine plant Zostera marina, and five 1 × 9 marsh edge plots were used to test Spartina alterniflora. No significant differences in plant growth or survival were observed between Boat Harbour and reference sediment after 2 months. Postexperiment analysis of contaminants (metals and polychlorinated dibenzo-p-dioxins, polychlorinated dibenzofurans (PCDD/Fs)) in both types of sediment and plant tissues showed similarly low levels of contaminants. Findings indicate that pre-industrial sediment in the harbor should be able to support plant growth after removal of overlying contaminated sediment.
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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 | 0.000 | 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) | 0.001 | 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 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".