Advancing Amphibian Conservation through Citizen Science in Urban Municipalities
Bibliographic record
Abstract
As cities adopt mandates to protect, maintain and restore urban biodiversity, the need for urban ecology studies grows. Species-specific information on the effects of urbanization is often a limiting factor in designing and implementing effective biodiversity strategies. In suburban and exurban areas, amphibians play an important social-ecological role between people and their environment and contribute to ecosystem health. Amphibians are vulnerable to threats and imbalances in the aquatic and terrestrial environment due to a biphasic lifestyle, making them excellent indicators of local environmental health. We developed a citizen science program to systematically monitor amphibians in a large city in Alberta, Canada, where 90% of pre-settlement wetlands have been removed and human activities continue to degrade, alter, and/or fragment remaining amphibian habitats. We demonstrate successes and challenges of using publicly collected data in biodiversity monitoring. Through amphibian monitoring, we show how a citizen science program improved ecological knowledge, engaged the public in urban biodiversity monitoring and improved urban design and planning for biodiversity. We outline lessons learned to inform citizen science program design, including the importance of early engagement of decision makers, quality control assessment, assessing tensions in program design for data and public engagement goals, and incorporating conservation messaging into programming.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".