Molluscan live-dead mismatch as a gauge of urban footprints and unpolluted baselines in cold waters hostile to shell preservation
Bibliographic record
Abstract
Assessing the health of benthic ecosystems is crucial for understanding anthropogenic impacts and guiding remediation efforts, but conventional methods relying upon a one-time sampling face many challenges in estimating the pre-impact state and identifying unaffected areas. Here, we used species-level abundance data for living and dead mollusks acquired from a single sampling (2014) of the monitoring grid for the Macaulay Point outfall (City of Victoria, British Columbia, Canada), which discharged untreated municipal wastewater into the cold-temperate Juan de Fuca Strait at 60 m water depth from the early 1970s until upgrading to tertiary treatment in 2020. The footprint of outfall-related nutrients was readily detected: despite corrosive seawater and scant sediment accumulation hostile to shell preservation, molluscan death assemblages retained a sufficient local inventory of shells produced under pre-pollution conditions, which favored suspension feeders, to create a gradient of live-dead mismatch with detritivore-dominated living assemblages. Concerns about postmortem bias against vulnerable shell types were also allayed, as were concerns that strong tidal currents would obscure or obliterate habitat-level differences. Live-dead mismatch was relatively and absolutely small in 2 reference areas, i.e. comparable to the offset expected from under-sampling natural temporal variability in the source living community. These results support using molluscan live-dead analysis to fill pernicious knowledge gaps outside the warm-water settings where the method was developed and to gauge (1) the spatial and ecological footprint of human stressors where data are scarce or suspect, (2) pre-stress baselines within that footprint, and (3) outlying areas that might serve as reference areas for monitoring systems going forward.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".