Benthic habitat mapping and sediment nutrient cycling in a shallow coastal environment of Nova Scotia, Canada
Bibliographic record
Abstract
Sedimentary facies and benthic metabolism of subtidal sediments were studied in a relatively small, but historically active bay in Southern Nova Scotia, Canada. Our study approach was based on the combination of benthic habitat mapping, field/lab experiments, and numerical models of sediment geochemistry. This approach provided an effective mean for ecosystem-scale assessments of key benthic processes (carbon recycling, denitrification, etc.). The distribution of bottom types and sediment properties was assessed using direct (grabs and core sampling) and remote (video and acoustic) sampling methods. The geo-referencing, classification, and interpolation of sediment properties (acoustic data, bathymetry, organic matter content, sediment porosity, etc.) allow to produce maps showing their spatial distribution, which instead served as input of numerical models oriented to predict carbon and nitrogen recycling rates at bay-scale. This approach become relevant given the commonly limited spatial and temporal resolution of biogeochemical measurements. Results are discussed in regard to the implications for coastal management (maintenance of ecosystem functioning), and understanding of coastal biogeochemical cycles. Data quality and accuracy of spatially interpolated data was also evaluated, including their impacts on model predictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".