Citizen Science and Sea Turtles: Using iNaturalist as a Tool to Study Chelonian Conservation Biology
Bibliographic record
Abstract
Sea turtles are among the most threatened group of marine animals, with all species at risk for an assortment of anthropogenic factors including pollution, over-fishing bycatch, and climate change. As the threats to these large aquatic reptiles continue to increase, populations may be facing declines in many areas of their native home ranges. Therefore, new techniques that allow for population monitoring alongside more traditional methods are needed to increase our understanding of these ocean-dwelling vertebrates. Due to their unique biology, sea turtles are often readily observed in near-shore waters and on beach habitats. I assessed the presence and general demography of sea turtles within the USA on the citizen science platform, iNaturalist and report on major findings related to mortalities, age classes, and states where observations are concentrated. iNaturalist observations in this publication represent 8,089 green, 1,171 loggerhead, 312 kemp’s ridley, 129 leatherback, 128 hawksbill, and 17 olive ridley sea turtles. Observations consisted primarily of adult age classes (85.9%), with moralities representing only 10.2%, and the majority of images showing beach habitat (55%) versus open water (44.8%). The number of observations increased annually indicating the potential for this citizen science application to provide valuable population trend data for conservation managers and future study of their nesting and behavioral biology worldwide.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".