The FACT Network: Philosophy, Evolution, and Management of a Collaborative Coastal Tracking Network
Bibliographic record
Abstract
Abstract The FACT Network (originally the Florida Atlantic Coast Telemetry working group), established in 2007, is a grassroots collaboration that is dedicated to improving the conservation and management of aquatic animals by facilitating data sharing amongst researchers using acoustic telemetry technology, providing a community for scientists, and building stakeholder partnerships. Founded along the eastern Florida coastline, FACT quickly grew in both membership and geographical range to include 93 partner groups along a large portion of the southern U.S. Atlantic seaboard and western Caribbean. This rapid expansion was facilitated by adapting FACT's policies and procedures to meet the growing needs of its members, including implementing an online data sharing system capable of exchanging information with other compatible systems designed by the Ocean Tracking Network (OTN). Less than 13 months from its inception, the FACT database housed 129.5 million detections and metadata for 5,979 tags from 101 projects (85 FACT projects and 16 OTN-based projects). Twice-yearly meetings allow FACT members to interact, building relationships between individuals, which in turn promotes collaboration and data sharing. The success of FACT is attributable to a combination of biogeographical factors; partnerships with the Animal Tracking Network, OTN, and Southeast Coastal Ocean Observing Regional Association; and active membership. In a survey of FACT members, data management services and belonging to a community ranked highest as reasons for joining the network. Future success of the FACT Network will depend on how effectively it can adapt to changing needs and conditions in the scientific landscape. In this paper, we describe the origins, philosophy, and management approach of the FACT Network, with the hope that this information can provide insights into the benefits (and limitations) of future acoustic tracking networks in other regions.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".