Ocean and Coastal Acidification Monitoring Priorities for the Northeast US and Eastern Canada
Bibliographic record
Abstract
The Interagency Working Group on Ocean Acidification monitoring Prioritization Plan 2024 calls for Coastal Acidification Networks to identify the ocean and coastal acidification (OCA) monitoring needs most important for their regions. The Northeast Coastal Acidification Network (NECAN) organized a webinar series to study regional needs, which culminated with a workshop in November 2023. This workshop led to the identification of six priority new Monitoring Needs in addition to the maintenance of current monitoring efforts: -Improve spatial and temporal scale of monitoring co-located OCA variables and biological measurements to better resolve variability of acidification dynamics in concert with biological processes -Increase subsurface monitoring to understand how conditions vary at depth -Increase the number of high-frequency monitoring assets that measure at least two of four carbon parameters -Increase near-real-time and rapid response observing capacity to capture episodic events -Determine fluxes and rates that would help parameterize and constrain regional modeling efforts to understand past conditions and project future trends -Increase spatial coverage of “climate”-quality observations This report presents monitoring needs and opportunities for consideration by coastal managers, decision makers, researchers, and monitoring groups. It offers options to apply new capacity or funding to the expansion of OCA monitoring in the NECAN region. Writing the report led to the identification of a number of cross-cutting actions which will lead to the implementation of these Monitoring Needs: ● Expand monitoring beyond carbonate chemistry to provide a complete assessment of OCA, its effects, and future trends. ● Enhance or leverage existing monitoring platforms for a cost-effective and collaborative approach to creating a more complete OCA monitoring system in the NECAN region. ● Expand the NECAN membership to include protected area experts, terrestrial biogeochemists and hydrologists, fisheries experts, social scientists, Tribal liaisons, project leads from large assessments, and other important stakeholders, rights holders and decision makers. ● Increase funding in the Northeast to both sustain currently-stretched efforts and grow a more robust ocean acidification monitoring program. ● Pursue immediate implementation of proxy approaches or interim strategies for measurements with technological or capacity limitations, while new technologies are being developed. ● Synthesize monitoring information to advance the collective understanding of OCA in the NECAN region. ● Deploy monitoring assets strategically, with end-user needs in mind, ensuring that the collected data is accessible, relevant, and useful for decision-making. ● Share NECAN’s experience in developing these recommendations with other regional CANs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".