Science response : aquaculture-eelgrass interactions
Bibliographic record
Abstract
Eelgrass is designated as an ecologically significant species in the southern Gulf of St. Lawrence (sGSL). It is suspected that eelgrass in this region is currently experiencing population change in many places. One potential driver of eelgrass declines in the sGSL is suspended shellfish aquaculture. However, interactions between cultured shellfish and eelgrass are complex and difficult to quantify and the overall risks of shellfish aquaculture to eelgrass in the region remain unclear. This Regional Science Response Process was requested by Aquaculture Management, Fisheries and Oceans Canada (DFO) to support the ongoing delivery of DFO’s role in assessing shellfish aquaculture interactions with fish and fish habitat under the Fisheries Act, as well as providing science advice to the Prince Edward Island aquaculture leasing program and Nova Scotia and New Brunswick provincial aquaculture regulators. The request for advice is to inform our understanding of the effects of suspended shellfish aquaculture on eelgrass beds. Specifically, the spatial interactions and likely effects of shellfish aquaculture on eelgrass habitat in the sGSL, with particular focus on shading from aquaculture gear; differentiated by species, gear type, and depth. While eelgrass can interact with shellfish aquaculture through a variety of pathways, this process focuses on the potential scope of shading on important eelgrass habitats within DFO Gulf Region. This was accomplished through a review of available scientific literature, the compilation and spatial analysis of data representing the distribution of aquaculture activities within eelgrass depth requirements, and field experiments measuring the effects of aquaculture gear on underwater light conditions. This Science Response Report results from the Regional Peer Review of March 30, 2023 on Characterizing the effects of suspended oyster aquaculture on eelgrass in the Gulf Region.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".