Using community science to advance grizzly bear conservation
Bibliographic record
Abstract
Alberta grizzly bears are classified as a threatened species in the province of Alberta as of 2010, with human-caused mortality and habitat loss a primary threat. The people who live, work, and recreate within bear habitat play a crucial role in their conservation. While the public is often enthusiastic about grizzly bears, and opportunistically report their observations to government staff, these reports are not systematic or rigorously collected and lack key information. As such, we developed GrizzTracker as a community science program. Following several years of successful deployment, we analyzed community scientist data and evaluated the efficacy of the program through an online user survey. We found that the GrizzTracker app was useful as a data collection and public engagement tool, yielding information for applied management, and that community scientists were generally satisfied. We provide considerations for future program development, including considerations for human, social, technological, and financial capital investment related to design, development, and implementation of data collection protocols, the importance of clearly communicating outcomes, and opportunities for educational outreach. While there is continued trepidation by traditionally trained scientists to develop or engage in community science programs, and some noted areas of improvement for our program specifically, we think that GrizzTracker offers a success story in community science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".