Social media-based community science for turtle nest monitoring and conservation
Bibliographic record
Abstract
In late 2020, a private volunteer group operating through a Facebook group approached Toronto and Region Conservation Authority (TRCA) with an interest in participating in the Citizen Science Nest Protection and Monitoring Volunteer Program. During the 2021 season, TRCA staff virtually trained volunteers in turtle nest detection, protection, and monitoring due to COVID-19 social constraints, and provided necessary supplies and equipment. In 2021 volunteers and staff detected 181 turtle nests and were able to protect 75 nests with simple predator-exclusion devices, while in 2022, 165 nests were detected, and 155 were protected. The volunteers also collected data, including nesting location, date, and species in addition to the number of successful hatchlings (2021 only). In 2021, the 75 protected nests yielded 81 Midland Painted Turtle and 665 Snapping Turtle hatchlings. Five Midland Painted Turtle’s eggs were also recovered from an injured turtle, and 44 Snapping Turtle eggs were recovered from a partially predated nest, transported to the Ontario Turtle Conservation Centre, incubated, hatched, and overwintered, and then released back into the local wetlands. The partnership with a local community group proved effective, and we would recommend this practice to other conservation organizations interested in turtle nest protection.
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 | 0.000 | 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 | 0.000 | 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".