Bibliographic record
Abstract
Twelve indicators have previously been identified to monitor the ecosystem-level conservation objectives for the Musquash MPA (i.e., productivity, biodiversity, habitat quality), as well as to monitor potential threats to these conservation objectives. For this review, twenty-three datasets were assessed for their progress/applicability to date in supporting the ongoing monitoring of the Musquash MPA, as per the established monitoring plan. This review concluded that these datasets provide a valuable foundation for monitoring the state of the Musquash Estuary ecosystem; however, further work is needed to develop these into a more consistent, long-term monitoring program with clear links between the conservation objectives, potential threats to these conservation objectives, and the effectiveness of management measures to prevent or mitigate these threats. It was recognized that there are other sources of information, in addition to the twenty-three datasets reviewed here, that contribute to the baseline characterization of the MPA. The incorporation of these datasets into the monitoring program requires additional discussion on how they can be developed into explicit indicators that are clearly linked to conservation priorities and/or potential threats. Currently, there are no established benchmarks against which to evaluate adequacy for each indicator in the Musquash MPA Monitoring plan. It was recommended that further consideration be given to the evaluation of indicators against thresholds, i.e., what is understood as meaningful change, especially in the context of a changing climate. It was agreed that the status and trends of Musquash MPA monitoring indicators also need to be considered and reported on within a broader regional context, i.e., within the context of the Bay of Fundy and Scotian Shelf-Bay of Fundy bioregional conservation network. Musquash monitoring is conducted by several external partners, in addition to DFO. Consequently, it was identified that further steps to standardize sampling protocols and downstream data analysis, and regularize reporting, were needed to support enhanced integration of the various monitoring data streams into a coherent assessment of status and efficacy. Gaps and uncertainties in baseline information were identified to guide future improvements for long-term monitoring of the MPA. Given the diversity of expertise and interest in this area, gaps were not prioritized at this meeting. Opportunities to fill these gaps will continue to be explored, and the results of exploratory monitoring indicators will continue to be assessed and presented for review at future meetings. Given the diversity of data sources that will be required to implement a long-term monitoring program, the development of robust data management and reporting strategies would help improve periodic assessments on the status of the MPA and its monitoring program
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 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".