State of fish and fish habitat reporting : part 2
Bibliographic record
Abstract
A synthesis of the available and relevant data on the State of Fish and Fish Habitat in the Ontario and Prairie Region (SOFFH-OPR), that focused on the Lower Great Lakes and Alberta East Slopes priority reporting areas, was conducted based on previously selected indicators (Biodiversity, Water Quality, Connectivity, Land Use and Land Cover, and Climate Change) and their constituent metrics (2 to 6 per indicator). The value of SOFFH-OPR metrics in the Lower Great Lakes and Alberta East Slopes varied among reporting areas and assessment units, but generally reflected geographical patterns in species richness and habitat, and the effects of agriculture, urbanization, resource extraction, and other development on watersheds. Data gaps limited reporting and led to uncertainty in the SOFFH-OPR for some metrics and assessment units. These gaps could be filled, and uncertainties managed, through increased spatial and temporal sampling and the continued development of standardized monitoring programs towards the measurement of metrics that influence and are sensitive to changes in aquatic ecosystem health. Overall scores of the SOFFH-OPR for each assessment unit or reporting area were not produced for this report. Combining metrics and indicators would require decisions related to the weighting of metrics to generate the overall scores. Additionally, the indicators and metrics selected may be differentially important to the various species, life stages, and habitat features. The development of reporting thresholds and classification schemes is not a requirement for reporting on the state of ecosystems (including SOFFH-OPR), but can support objectivity, simplify communication with non-specialist audiences, and can help to integrate data from multiple jurisdictions. However, developing classification schemes is associated with several challenges including ignoring important differences among habitat types, and exaggerating differences among data points that fall close to, but on opposite sides of, a reporting threshold value. Classification schemes for reporting on the SOFFH could be based on functional relationships with management objectives, thresholds established in other guidelines, policy, regulations or other reporting initiatives, relative ranking, or expert opinion. Where management objectives are quantitatively defined, and the relationship between management objectives and metrics is known, developing classification schemes based on management objectives is the recommended method as it allows for SOFFH reporting to be aligned with management activities. Comparing multiple metric approaches is valuable to ensure synergy across management and ecosystem objectives and thresholds. Reporting thresholds (i.e., values of a metric used to define different categories of ecosystem state) are not necessarily equivalent with ecological thresholds (i.e., values of a metric beyond which ecosystems show rapid or categorical change, also known as ‘tipping points’). Not all metrics or indicators demonstrate ecological thresholds, and even when they exist, it will not always be appropriate to equate the two concepts. Reporting on the SOFFH-OPR should be accompanied by reporting on the quality, uncertainty, and representativeness of data. Similar reporting initiatives have used checklists that include sample size, the recency and temporal range, and the geographic coverage of the dataset to estimate the quality of data. Data quality considerations for SOFFH-OPR could include power analyses, species accumulation curves for fish species richness, and evaluation of data resolution and monitoring design. Data limitations and challenges with developing reporting thresholds resulted in a number of uncertainties related to the metrics within the reporting areas of SOFFH-OPR. Improved and expanded geospatial data, targeted research, and adaptive management can address uncertainties and knowledge gaps. The information presented here is a synthesis of data related to the current SOFFH-OPR. As conditions, environmental drivers, and scientific knowledge change, the SOFFH in the two reporting areas are likely to change and may need to be reassessed. Reporting on the SOFFH-OPR should also be informed by other sources of knowledge (including Indigenous and local knowledge), which could help address informational gaps and inform our understanding of historical and desired ecosystem states.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.034 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".