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Record W3087064677 · doi:10.1002/mcf2.10100

The FACT Network: Philosophy, Evolution, and Management of a Collaborative Coastal Tracking Network

2020· article· en· W3087064677 on OpenAlexaff
Joy Young, Mary E. Bowers, Eric A. Reyier, Danielle Morley, Erick R. Ault, Jonathan Pye, Riley M. Gallagher, Robert D. Ellis

Bibliographic record

VenueMarine and Coastal Fisheries · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsOcean Tracking NetworkDalhousie University
Fundersnot available
KeywordsMetadataGrassrootsStakeholderGeographyTracking (education)Environmental resource managementComputer scienceBusinessWorld Wide WebPolitical scienceEnvironmental sciencePublic relations

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0070.004
Scholarly communication0.0110.013
Open science0.0030.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.016
GPT teacher head0.206
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2020
Admission routes1
Has abstractyes

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