Metadata for the Marine Turtle Tagging and Monitoring Program by Caño Palma Biological Station
Bibliographic record
Abstract
This metadata repository describes long-term data collected by Caño Palma Biological Station and the Canadian Organization for Tropical Education and Rainforest Conservation (COTERC). The dataset spans 2006–present and is collected at Playa Norte, Costa Rica, an important Atlantic rookery. Green (Chelonia mydas), hawksbill (Eretmochelys imbricata), and leatherback turtles (Dermochelys coriacea) are the most common species in the database, with infrequent observations of loggerheads (Caretta caretta). The data pertain to the following topics: All activity: Beach activity by nesting females is collected daily. When a nesting female is encountered, nest information, tag data, and body health information is collected. This data includes a tag database of all individuals observe on the study transect.Nest excavations: After a nest hatches or expires, information on embryo stage, predation, important incubation events, deformities, and nest success is recorded. Human impact: By Costa Rican law, human activity on the Playa Norte is prohibited during the sea turtle nesting season except with a permit. Data on the types, times, and locations of human activity observed during nighttime surveys is recorded.Survey effort: The effort expended by all field teams (i.e. morning, night, and excavation surveys) is recorded.Non-standardized data: This pertains to data that does not have a standardized format and does not exist for most years, and thus metadata is not included in this repository. Notably, daily nest status data contains detailed longitudinal information on events occurring throughout the incubation of each marked nest. For questions about the data or metadata, or if interested in collaborating or accessing this data, please email dataintergrity@coterc.org. More information about COTERC and Caño Palma Biological Station can be found at coterc.org.Peer reviewed publications using this dataset include:Damian, M., Harris, A., Aussage, J., & Fraser, G. S. (2022). Seasonal deposition of marine debris on an important marine turtle nesting beach in Costa Rica. Marine Pollution Bulletin, 177, 113525.Restrepo, J., Rojas-Cañizales, D., & Valverde, R. A. (2022). Historical Records of Loggerhead Sea Turtle (Caretta caretta) Nesting at Tortuguero, Costa Rica. Journal of Herpetology, 56(3), 336-340.Pheasey, H., Glen, G., Allison, N. L., Fonseca, L. G., Chacón, D., Restrepo, J., & Valverde, R. A. (2021). Quantifying illegal extraction of sea turtles in Costa Rica. Frontiers in Conservation Science, 2, 705556.Pheasey, H., Roberts, D. L., Rojas-Cañizales, D., Mejías-Balsalobre, C., Griffiths, R. A., & Williams-Guillen, K. (2020). Using GPS-enabled decoy turtle eggs to track illegal trade. Current Biology, 30(19), R1066-R1068.Pheasey, H., McCargar, M., Glinsky, A., & Humphreys, N. (2018). Effectiveness of concealed nest protection screens against domestic predators for green (Chelonia mydas) and hawksbill (Eretmochelys imbricata) sea turtles. Chelonian Conservation and Biology, 17(2), 263-270.Velez-Espino, A., Pheasey, H., Araújo, A., & Fernández, L. M. (2018). Laying on the edge: demography of green sea turtles (Chelonia mydas) nesting on Playa Norte, Tortuguero, Costa Rica. Marine Biology, 165(3), 1-12.
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.110 | 0.071 |
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".