Mapping areas of interest for coastal enhancement related to \ncapelin spawning in Conception Bay, Newfoundland and Labrador
Bibliographic record
Abstract
Capelin is a forage fish species that plays a key role in Newfoundland and Labrador (NL) marine ecosystems. The species tends to spawn on beaches possessing specific environmental characteristics, such as gentle slopes and granular sediment sizes. Many beaches on the island of Newfoundland have been modified by humans and are no longer suitable for capelin spawning. Coastal enhancement work, which could help increase the suitability of beaches for capelin spawning, requires an understanding of beach dynamics and geomorphology to identify suitable sites for enhancement and long-term effectiveness. Three beaches along Conception Bay, NL were examined to inform potential future coastal enhancement work: Lance Cove, Chapel’s Cove, and Harbour Main. Aerial photos and digital surface models (DSM) of the beaches were acquired at different times throughout 2019 using an uncrewed aerial vehicle, complemented by field observations. A multi-criteria decision analysis (MCDA) approach was then used to provide a systematic way to prioritize these beaches for enhancement suitability. The MCDA prioritization accounts for key components of capelin spawning, wind direction, beach protection, sediment grain size, slope, and anthropogenic footprint. \nKey components were addressed both in terms of capelin spawning suitability and coastal stability. DSM and geomorphological data indicate that beaches experience different changes throughout a season, although they present somewhat similar physical characteristics. Adjacent beaches can exhibit very different responses to the same weather event, indicated by the geomorphic dynamics of Lance Cove, Chapel’s Cove, and Harbour Main. Of the three sites, Chapel’s Cove is most suitable for coastal enhancements to make the beach more suitable for capelin spawning. Chapel’s Cove is more dynamically stable than Lance Cove, and is much less anthropogenically influenced than is Harbour Main. Findings demonstrate the key factors \ninfluencing beach geomorphology and how it pertains to planning species-specific enhancement projects, building on the idea that enhancement projects require a multi-dimensional approach.
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How this classification was reachedexpand
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".