Assessing the Benefits, Challenges and Scientific Value of Community Science Programs: A Case Study Using Bumble Bee Watch
Bibliographic record
Abstract
We are experiencing a biodiversity crisis but resources to help species are limited. Scientists are turning to community science to complement traditional scientific methods. Bumble bees (Bombus spp.) are important pollinators in temperate regions, but many are in decline, and more information is needed to conserve them. The Bumble Bee Watch (BBW) program collects this through photos submitted by volunteers and identified by experts. Yet many community science programs struggle. Chapter 2 reviews common successes and challenges, offering best practices for developing and running programs. \n \nTo determine whether BBW is filling knowledge gaps, Chapter 3 compares its data to the Bumble Bees of North America database (BBNA) over all years and 2010-2020. BBW recorded 41 species (BBNA had 48) from all parts of the continental US and Canada, confirmed persistence, and provided novel locations for species outside of and within the known extent of occurrence. BBW showed its greatest impact from 2010-2020 by contributing 25% of all records, 28% of all unique locations, and 32% new plant forage genera. \n \nBBW does not replace traditional surveys, but does complement them. Chapter 4 shows that B. pensylvanicus is critically endangered in Canada according to IUCN Red List criteria. BBW provided 20% of all B. pensylvanicus records and 36% of its sites over the 2007-2016 period assessed, and thus provided important information on its current abundance and distribution. \n \nNo experience is required to participate in BBW, but having participants able to accurately identify species is beneficial. Chapter 5 explores the percent agreement and veracity of participant species identifications compared to experts, with the average being 53% and 56%, respectively. With better educational resources, participants may be better trained to identify species more accurately. \n \nUnderstanding the motivations and insights of community science participants is important. Chapter 6 discusses the results of a BBW user and expert survey: participants want to contribute to science and save the bees, and report an increase in knowledge and skills after participating. Although areas for improvement are noted, BBW is an important tool for Bombus researchers, and demonstrates the value that community science has for species conservation.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".