Braun et al., 2023 - Google Patents
Building use‐inspired species distribution models: using multiple data types to examine and improve model performanceBraun et al., 2023
View PDF- Document ID
- 2123999325951601105
- Author
- Braun C
- Arostegui M
- Farchadi N
- Alexander M
- Afonso P
- Allyn A
- Bograd S
- Brodie S
- Crear D
- Culhane E
- Curtis T
- Hazen E
- Kerney A
- Lezama‐Ochoa N
- Mills K
- Pugh D
- Queiroz N
- Scott J
- Skomal G
- Sims D
- Thorrold S
- Welch H
- Young‐Morse R
- Lewison R
- Publication year
- Publication venue
- Ecological Applications
External Links
Snippet
Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage …
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- G06Q10/00—Administration; Management
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- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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- G06—COMPUTING; CALCULATING; COUNTING
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- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
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- G06Q10/00—Administration; Management
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- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
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