The Makings of a High Resolution Coastal Monitoring Program
Bibliographic record
Abstract
Typical ocean monitoring methods such as vessel transects, remote sensing, and single depth stationary platforms rarely provide the resolution required to characterize coastal ocean dynamics. Data collection at high temporal frequencies over multiple years, at high vertical and horizontal spatial resolution, is needed to capture changes at biologically relevant scales for aquaculture and other applications. The Centre for Marine Applied Research (CMAR) operates a Coastal Monitoring Program to fill this critical gap in ocean data for the province of Nova Scotia, Canada. This Program maintains a network of nearshore oceanographic stations that measure Water Quality variables (temperature, dissolved oxygen, salinity) several times per hour, with continuous time series up to 8 years in length. Coastal Current and Wave data measured by provincial partners are also integrated into the Program, with CMAR providing data processing services. CMAR has developed a suite of R packages to partially automate data processing, quality control, and visualization for all data branches. Clear and consistent Data Management procedures ensure production of high-quality data products. Summary reports with deployment details and data figures designed for “at-a-glance” assessment are available on the CMAR website, and complete datasets can be downloaded for free from online repositories. These data products have a diverse range of users and help support industry, governance, and research. Data collection is on-going, with efforts to continue building long-term datasets, increase spatial coverage, and monitor additional variables.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".