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A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags.

Ian D JonsenToby A PattersonDaniel P CostaPhilip D DohertyBrendan J GodleyW James GrecianChristophe GuinetXavier HoennerSarah S KienlePatrick W RobinsonStephen C VotierScott WhitingMatthew J WittMark A HindellRobert G HarcourtClive R McMahon
Published in: Movement ecology (2020)
Our model provides quality-controlled locations from Argos Least-Squares or Kalman filter data with accuracy similar to or marginally better than Argos Kalman smoother data that are only available via fee-based reprocessing. Simplicity and ease of use make the model suitable both for automated quality control of near real-time Argos data and for manual use by researchers working with historical Argos data.
Keyphrases
  • quality control
  • electronic health record
  • big data
  • machine learning
  • deep learning
  • artificial intelligence
  • sensitive detection