Identifying community practices in marine benthic data usage in Florida
Bibliographic record
Abstract
A significant proportion of Florida's population lives on the coast and is directly impacted by alterations to the coastal zone, weather disasters ( e.g. , hurricanes, erosion, flooding), or changes to ecosystem services. Data collected in Florida waters (including water quality, habitat health, bathymetry, and fisheries data) are important for the maintenance of coastal waters, communities, and ecosystems. Yet benthic data collected by a variety of stakeholders are often not shared or openly available, with little metadata to ease data reuse, and are often stored in incompatible formats. To assess the needs of government agencies, private companies, academic researchers, and data managers, we conducted a survey and organized an expert focus group to determine the current state of coastal and marine data usage and distribution in Florida. Through the survey, we asked participants to describe the types of data they use or collect, how they use that data, what limitations they encounter with data sharing, how and when they share their data, and what sorts of metadata standards they use in their work. We determined that many data producers and users are unaware of data standards and often do not follow best management practices for data collection and sharing. The sector of activity of the individual respondent (government, academic, non-profit) determined how data users were interacting with or collecting data and what standards they followed when sharing data. Our expert panel largely echoed our findings, with consistent, well-documented, and standardized datasets being the most important components for data integration in projects. To advance accessibility and reusability of benthic data, our project highlights the need for additional training of stakeholders on data standardization, collaboration and integration, which needs to be applied across institutions. A major need that was identified is tools that make data sharing and metadata creation easier and more efficient.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".