Characterization of Near-Shore Marine Vegetative Habitat Throughout Fortune Bay and Bay d'Espoir (2023-2025)
Why this work is in the frame
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Bibliographic record
Abstract
Characterization of near-shore marine vegetative habitat throughout Fortune Bay and Bay d'Espoir (2023) Data collection began in 2023, on targeted components of coastal ecosystems throughout Fortune Bay, Connaigre Bay and Bay d'Espoir to develop a baseline dataset characterizing seagrass, macroalgae and other habitats created by structure-providing species. This effort will continue through 2027. Throughout ten sites of this region, transect data are collected detailing seagrass and macroalgae species frequency, abundance, and distribution. As part of this characterization, marine sediments are documented along each transect, and in-situ water quality measurements. Additionally, pole seines are conducted at each site to collect baseline data on the presence and diversity of fish and invertebrate species present within these habitats. 1) Seagrass and Macroalgae: Distribution, frequency, and abundance of seagrass and macroalgae are monitored at ten sites. Three transects are surveyed at each site semi-annually to record seasonal growth. 2) Species Inventory: Pole seines are conducted annually at each site to collect baseline data on nearshore fish and invertebrate assemblages. If aquatic invasive species are found during a survey, they are recorded and reported. 3) Marine Sediment: Throughout each transect surveys for seagrass and macroalgae frequency and abundance, estimates of the composition of marine sediment found present and its depth along each transect are recorded at 5 meter intervals. 4) Water Quality: In-situ water quality data is collected at the beginning and end of each transect surveyed. Parameters recorded include dissolved oxygen, pH, salinity, conductivity, temperature and turbidity. 72-hour rainfall and wave conditions of the site at the time of each survey are also recorded. 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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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