Using NatureCounts to Support the Kunming-Montreal Global Biodiversity Framework in Canada
Bibliographic record
Abstract
Targets 20 and 21 of the Kunming-Montreal Global Biodiversity Framework establish that access to good data and innovative data products are crucial to halting and reversing biodiversity loss, and that biodiversity data access has implications for all targets of the framework. Birds Canada’s NatureCounts platform*1 seeks to meet these needs by supporting easy and accurate data collection, interpreting data to produce meaningful knowledge and data products, and sharing data according to the FAIR principles (Findable, Accessible, Interperable, Reusable) to support conservation action and policy. NatureCounts supports the collection of robust biodiversity data by professional and volunteer-based monitoring programs. The NatureCounts mobile app and web interface are customizable data collection solutions that integrate standardized data from the field directly into a sharing-ready repository using a standardized schema. The flexible architecture accommodates nearly any monitoring protocol, while a user-friendly interface, unique tools, and instantaneous data upload incentivize adoption, encouraging FAIR data participation by projects of all types and sizes. Data collected using these tools are uploaded to the NatureCounts database. Hosting over 250 million records, this massive repository holds endless potential for conservation applications. Tools including an online data explorer and R package facilitate easy data access by researchers and conservationists. Flexible data access permissions support the security of sensitive records and Indigenous data sovereignty. Various data products support research and conservation, and directly address the targets of the Global Biodiversity Framework. For example, a dedicated workflow underpins the process of identifying Canada’s Key Biodiversity Areas—spaces designated as vital to the conservation of biodiversity in Canada—in accordance with Target 3. Another uses the data to set and evaluate federal population goals for Canada’s birds for the federal government, integrating biodiversity into decision making as per Target 14. A third feeds data directly into the Canadian process for identifying endangered species, addressing extinction risk as specified in Target 4 and seamlessly connecting data collection to policy. NatureCounts also processes over 9000 requests for raw data yearly by the conservation community. Users query and filter the data, then access them either through a browser-based download portal or the dedicated naturecounts R package.*2 To help NatureCounts data users interpret raw data, the Birds Canada GitHub page*3 contains publicly available repositories and documents that detail workflows for processing and analyzing data accessed through NatureCounts. These well-documented, tested, and easily shared repositories ensure reproducible research practices. To date, data from NatureCounts have supported over 4200 scientific publications and an immeasurable amount of unpublished work. Data from NatureCounts are used for species assessments, land use planning, impact assessment, academic research, climate change mitigation, and much more, allowing data from NatureCounts to be used in pursuit of nearly every target in the framework. Through the ongoing development of NatureCounts, Birds Canada aims to fulfill the goals of the Kunming-Montreal Global Biodiversity Framework, and make measurable progress for biodiversity in Canada.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".