Citizen science and roadkills: trends along project lifespan and comparison of tropical and temperate projects
Bibliographic record
Abstract
The collection of scientific data by people without a science degree is at least as old as Antonie van Leeuwenhoek, but thanks to smartphones it now involves large numbers of volunteers, leading to studies about who the so called “citizen scientists” are, how they behave, and how to improve their work. There are, however, no worldwide studies about citizen science projects reporting fauna killed in road collisions. Here we analyze data from the 31 projects available in September 2017 in iNaturalist.org, the largest website for this subject. The USA and Europe have the most projects, but after correcting for population size, countries like Costa Rica and Canada are outstanding, possibly thanks to widespread Internet access and high educational levels. Projects had a mean of 431 observations, 48 species, of 32 volunteers who, on average, posted 19 observations each. Most volunteers contributed few records and were active only briefly. The roadkill data shows that, in the tropics, seasonal mortality trends match the movement of animals in search of water for drinking and for reproduction, while in temperate sites project differences depended mostly on which particular species is studied. We recommend future consideration of how the behavior of volunteers and projects changes along time, a subject that has seldom been considered in previous studies
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".