A Biodiversity Observation Network to support conservation action and mainstream knowledge in Canada
Bibliographic record
Abstract
Canada has begun an ambitious project to build an observing system to monitor the changing state of its biodiversity and ecosystems. A Canada-wide Biodiversity Observation Network (CAN BON) can support the measurement, mapping, and modelling of biodiversity change—the losses and gains in the diversity of plant, animal, and microbial life—and ecosystem services. This initiative responds to eight challenges presently constraining Canada's capacity to deliver timely and robust knowledge to achieve its biodiversity goals. CAN BON is conceived as a network connecting diverse organizations to support sustained biodiversity monitoring by collaboration among universities, museums, governments, industries, NGOs, community groups, and Indigenous organizations. This inclusive network will “mobilize monitoring data” to (1) combine observation and computing infrastructures and traditional knowledge to track and understand biodiversity losses and gains across the country; and (2) link the accumulated data and knowledge to models to inform the detection and attribution of biodiversity change needed to support biodiversity policy with forecasts from local to national levels. We expect that CAN BON will foster the mainstreaming of biodiversity data and knowledge into other sectors of the economy and society, and thereby support the technical and social innovation in Canada's transition to a nature-positive future.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".