Citizen Science Surveys Provide Novel Nearshore Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 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 it