Citizen Science Surveys Provide Novel Nearshore Data
Bibliographic record
Abstract
Abstract Long-term data are key to understanding how species, communities, and habitats change over time. Citizen science programs can support data collection at greater spatial and temporal scales than other types of scientifically collected data, which tend to be project specific and are often tied to short funding periods. This is particularly true for environments that are difficult to sample, such as subtidal ecosystems. The Reef Environmental Education Foundation's (REEF) citizen science SCUBA surveyors have been collecting fish, invertebrate, and algae data in British Columbia since 1998. This study demonstrates how citizen science data from REEF can be used to answer scientific questions via two case studies: the first on Lingcod Ophiodon elongatus population responses to management decisions and the second on detecting rockfish Sebastes spp. young-of-year abundance pulses. The results of these case studies suggest that data from REEF, despite their limitations, can be used to improve our understanding of nearshore marine ecosystems.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".