Facilitating an ecosystem approach through open data and information packaging
Bibliographic record
Abstract
Abstract Open data that can be easily incorporated into analyses are essential for developing ecosystem approaches to marine ecological management: a common goal in fisheries policy in many countries. Although it is not always clear what constitutes an ecosystem approach, it always involves scientists working with a large variety of data and information, including data from physical and oceanographic sampling, multispecies surveys, and other sources describing human pressures. This can be problematic for analysts because these data, even when available, are often held in disparate datasets that do not necessarily correspond at appropriate temporal and spatial scales. Data can often only be obtained by specific requests to individuals in governmental agencies who are delivering on an increasing number of data requests as interest grows in practical ecosystem approach implementation. This data access model is not sustainable and hinders the momentum for ecosystem approach development. We describe a data bundling R package that makes data and climate projections available at appropriate scales to facilitate development of an ecosystem approach for the Gulf of St. Lawrence, Canada. This approach integrates closely with the present workflow of most government analysts, academics in fisheries, and scientists in private industry. The approach conforms with open data initiatives and makes data easily available globally while relieving some of the burden of data provision that can fall to some individuals in government laboratories. The structure and approach are generic, adaptable, and transferable to other regions and jurisdictions.
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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 | 0.056 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".