Assessing the representation of species included within the Canadian Living Planet Index
Bibliographic record
Abstract
To effectively combat the biodiversity crisis, we need ambitious targets and reliable indicators to accurately track trends and measure conservation impact. In Canada, the Living Planet Index (LPI) has been adapted to produce a national indicator by both World Wildlife Fund-Canada (Canadian Living Planet Index; C-LPI) and Environment and Climate Change Canada (Canadian Species Index) to provide insight into the status of Canadian wildlife, by evaluating temporal trends in vertebrate population abundance. The indicator includes data for just over 50% of Canadian vertebrate species. To assess whether the current dataset is representative of the distribution of life history characteristics of Canadian wildlife, we analyzed the representation of species-specific biotic variables (i.e., body size, trophic level, lifespan) for vertebrates within the C-LPI compared to native vertebrates lacking LPI data. Generally, there was considerable overlap in the distribution of biotic variables for species in the C-LPI compared to native Canadian vertebrate species lacking LPI data. Nevertheless, some differences among distributions were found, driven in large part by discrepancy in the representation of fishes—where the C-LPI included larger-bodied and longer-lived species. We provide recommendations for targeted data collection and additional analyses to further strengthen the applicability, accuracy, and representativity of biodiversity indicators.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".