SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks von Pearse, Alan R, McGree, James M, Som, Nicholas A, Leigh, Catherine, Maxwell, Paul, Ver Hoef, Jay M, Peterson, Erin E, Bryhn, Andreas C, .

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Using virtual reality to estimate aesthetic values of coral reefs von Vercelloni, Julie, Clifford, Sam, Caley, M. Julian, Pearse, Alan R., Brown, Ross, James, Allan, Christensen, Bryce, Bednarz, Tomasz, Anthony, Ken, González-Rivero, Manuel, Mengersen, Kerrie, Peterson, Erin E.

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IDW‐Plus: An ArcGIS Toolset for Calculating Spatially Explicit Watershed Attributes for Survey Sites von Peterson, Erin E., Pearse, Alan R.

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Rapid spatial risk modelling for management of early weed invasions: Balancing ecological complexity and operational needs von Froese, Jens G., Pearse, Alan R., Hamilton, Grant, Silvestro, Daniele

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Discussion on “Competition on Spatial Statistics for Large Datasets” von Vu, Quan, Cao, Yi, Jacobson, Josh, Pearse, Alan R., Zammit-Mangion, Andrew

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Inter-reef Halimeda algal habitats within the Great Barrier Reef support a distinct biotic community and high biodiversity von McNeil, Mardi, Firn, Jennifer, Nothdurft, Luke D., Pearse, Alan R., Webster, Jody M., Roland Pitcher, C.

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Giant coral reef fishes display markedly different susceptibility to night spearfishing von Pearse, Alan R., Hamilton, Richard J., Choat, John Howard, Pita, John, Almany, Glenn, Peterson, Nate, Hamilton, Grant S., Peterson, Erin E.

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Optimal prediction of positive-valued spatial processes: Asymmetric power-divergence loss von Pearse, Alan R., Cressie, Noel, Gunawan, David

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IDW ‐Plus: An Arc GIS Toolset for Calculating Spatially Explicit Watershed Attributes for Survey Sites von Peterson, Erin E., Pearse, Alan R.

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Modelling imperfect presence data obtained by citizen science von Mengersen, Kerrie, Peterson, Erin E., Clifford, Samuel, Ye, Nan, Kim, June, Bednarz, Tomasz, Brown, Ross, James, Allan, Vercelloni, Julie, Pearse, Alan R., Davis, Jacqueline, Hunter, Vanessa

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Optimal prediction of positive-valued spatial processes: asymmetric power-divergence loss von Pearse, Alan R, Cressie, Noel, Gunawan, David

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Using virtual reality to estimate aesthetic values of coral reefs von Vercelloni, Julie, Clifford, Sam, Caley, M Julian, Pearse, Alan R, Brown, Ross, James, Allan, Christensen, Bryce, Bednarz, Tomasz, Anthony, Ken, González-Rivero, Manuel, Mengersen, Kerrie, Peterson, Erin E

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Optimal Spatial Prediction for Non-negative Spatial Processes Using a Phi-divergence Loss Function von Cressie, Noel, Pearse, Alan R., Gunawan, David

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SSNdesign -- an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks von Pearse, Alan R, McGree, James M, Som, Nicholas A, Leigh, Catherine, Hoef, Jay M. Ver, Maxwell, Paul, Peterson, Erin E

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SSNdesign -- an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks von Pearse, Alan R, McGree, James M, Som, Nicholas A, Leigh, Catherine, Ver Hoef, Jay M, Maxwell, Paul, Peterson, Erin E

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Data from: Giant coral reef fishes display markedly different susceptibility to night spearfishing von Pearse, Alan R., Hamilton, Richard J., Choat, John Howard, Pita, John, Almany, Glenn, Peterson, Nate, Hamilton, Grant S., Peterson, Erin E.

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Data from: Using virtual reality to estimate aesthetic values of coral reefs von Vercelloni, Julie, Clifford, Sam, Caley, M. Julian, Pearse, Alan R., Brown, Ross, James, Allan, Christensen, Bryce, Bednarz, Tomasz, Anthony, Ken, González-Rivero, Manuel, Mengersen, Kerrie, Peterson, Erin E.

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Dataset for: Modelling imperfect presence data obtained by citizen science von Mengersen, Kerrie, Peterson, Erin, Clifford, Samuel, Ye, Nan, Kim, June, Bednarz, Tomasz, Brown, Ross, James, Allan, Vercelloni, Julie, Pearse, Alan R, Davis, Jacqueline, Hunter, Vanessa

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Clinically applicable deep learning for diagnosis and referral in retinal disease von De Fauw, Jeffrey, Ledsam, Joseph R., Romera-Paredes, Bernardino, Nikolov, Stanislav, Tomasev, Nenad, Blackwell, Sam, Askham, Harry, Glorot, Xavier, O’Donoghue, Brendan, Visentin, Daniel, van den Driessche, George, Lakshminarayanan, Balaji, Meyer, Clemens, Mackinder, Faith, Bouton, Simon, Ayoub, Kareem, Chopra, Reena, King, Dominic, Karthikesalingam, Alan, Hughes, Cían O., Raine, Rosalind, Hughes, Julian, Sim, Dawn A., Egan, Catherine, Tufail, Adnan, Montgomery, Hugh, Hassabis, Demis, Rees, Geraint, Back, Trevor, Khaw, Peng T., Suleyman, Mustafa, Cornebise, Julien, Keane, Pearse A., Ronneberger, Olaf

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Predicting conversion to wet age-related macular degeneration using deep learning von Yim, Jason, Chopra, Reena, Spitz, Terry, Winkens, Jim, Obika, Annette, Kelly, Christopher, Askham, Harry, Lukic, Marko, Huemer, Josef, Fasler, Katrin, Moraes, Gabriella, Meyer, Clemens, Wilson, Marc, Dixon, Jonathan, Hughes, Cian, Rees, Geraint, Khaw, Peng T., Karthikesalingam, Alan, King, Dominic, Hassabis, Demis, Suleyman, Mustafa, Back, Trevor, Ledsam, Joseph R., Keane, Pearse A., De Fauw, Jeffrey

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