Resultados de búsqueda - "LEARNING"

Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? por Seymour, Greg, Follett, Lendie, Henderson, Heath, Ferguson, Nathaniel

Materias: “…machine learning…”
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Advanced Prediction of Rice Yield Gaps Under Climate Uncertainty Using Machine Learning Techniques in Eastern India por Sahoo, Satiprasad, Singha, Chiranjit, Govind, Ajit

Materias: “…machine learning…”
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Tester selection for combining ability estimation of storage root yield and sweetpotato virus disease in sweetpotato breeding por Swanckaert, J., Santos, I.G.D., Chaves, S.F.S., Ssali, R.T., Mwanga, R.O.M., Azevedo, C.F., Mendes, T., Boeck, B. de, Eyzaguirre, R., Kitavi, M., Gemenet, D., Andrade, M.I., Grüneberg, W.J., Lindqvist-Kreuze, H., Yencho, George Craig, Campos, Hugo, Silva Pereira, G. da

Materias: “…machine learning…”
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Standard Operating Procedure (SOP) for digital imaging and quantification of banana weevil damage using Ilastik and ImageJ (Fiji) por Mwanje, G., Shah, T., Swennen, R., Nakato, G.V.

Materias: “…machine learning…”
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Predicting aflatoxin risk in maize using machine learning and satellite data in East and Southern Africa por Gachoki, S., Muthoni, F.K., Mahuku, G., Atehnkeng, J., Njeru, N., Kamau, J., Tripathi, L.

Materias: “…machine learning…”
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Summary of Learning on Monitoring, Evaluation, Learning and Impact Assessments (MELIA) por Gullotta, Gaia, Jersild, Amy, Templeton, Deborah, Negroustoueva, Svetlana

Materias: “…learning…”
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Ground reference dataset for crop type mapping and monitoring in four districts of Rwanda por Kenduiywo, Benson Kipkemboi, Wahome, Anastasia Mumbi, Sande, Stephen Ngondi, Chemutt, Joseph Kipkoech

Materias: “…machine learning…”
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Quick-reference guide for designing and scaling capacity-building programs on climate adaptation–related topics por Tierney, Nikki

Materias: “…learning…”
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Demand intelligence portal - annotated wireframe documentation por Sartas, M., Konlambigue, M.

Materias: “…machine learning…”
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Risk Management and Resilience: Learning on CGIAR's Ways of Working por CGIAR Independent Advisory and Evaluation Service

Materias: “…learning…”
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Rainfed wheat extent (2020/21) across Ethiopia's complex and highly fragmented agricultural smallholder landscape por Blasch, Gerald, Lesne, Louise, Wolter, Jolan, Alemayehu, Yoseph, Bontemps, Sophie, Hodson, David, Defourny, Pierre

Materias: “…machine learning…”
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Participatory modelling to enhance social learning, collective action and mobilization among users of the Mafungautsi forest, Zimbabwe por Standa-Gunda, W., Mutimukuru, T., Nyirenda, R., Prabhu, Ravi, Haggith, M., Vanclay, J.K.

Materias: “…social learning…”
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Discussion and conclusions por McDougall, C., Prabhu, Ravi, Fisher, R.

Materias: “…social learning…”
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Unlocking the potential of social learning for climate change and food security: Wicked problems and non-traditional solutions por CGIAR Research Program on Climate Change, Agriculture and Food Security

Materias: “…learning…”
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Climate change and social learning (CCSL): supporting local decision making for climate change, agriculture and food security por Carlile, Liz, Ballantyne, Peter G., Ensor, Jonathan, Förch, Wiebke, Garside, Ben, Harvey, Blane, Patterson, Zachary, Thornton, Philip K., Woodend, John

Materias: “…learning…”
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Making millets matter in Madhya Pradesh por Mondal, A., King, I.O., Roy, S., Priyam, S., Meldrum, G., Padulosi, S., Mishra, S.

Materias: “…experimental learning…”
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Applying social learning where ‘business-as-usual’ solutions no longer work for complex problems and programs por Le Borgne, Ewen

Materias: “…learning…”
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Can CoP theory be applied? Exploring praxis in a community of practice on gender por Bailey, Arwen

Materias: “…learning…”
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Troubleshooting methods, interpretation and reporting of results lab diagnosis por Beed, Fenton D., Ramathani, I.

Materias: “…learning material…”
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Is it possible to measure the welfare of the ridden horse? por Björk, Nicola

Materias: “…learning…”