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dc.contributor.authorCarral Couce, Luis
dc.contributor.authorTarrío-Saavedra, Javier
dc.contributor.authorVega Sáenz, Adán
dc.contributor.authorBogle, Johnny
dc.contributor.authorAlemán, Gabriel
dc.contributor.authorNaya, Salvador
dc.date.accessioned2021-06-08T15:15:59Z
dc.date.available2021-06-08T15:15:59Z
dc.date.issued2021-05
dc.identifier.citationCarral, L., Tarrío-Saavedra, J., Sáenz, A., Bogle, J., Alemán, G., & Naya, S. (2021). Modelling operative and routine learning curves in manoeuvres in locks and in transit in the expanded Panama Canal. Journal of Navigation, 74(3), 633-655. doi:10.1017/S0373463320000727
dc.identifier.issn1469-7785
dc.identifier.urihttp://hdl.handle.net/2183/28063
dc.descriptionFinanciado para publicación en acceso aberto: Universidade da Coruña/CISUGes_ES
dc.description.abstract[Abstract] Piloting in the Panama Canal is exceptional as, due to its importance, the functions of the captains of vessels are taken over by pilots. Hence, prior to inauguration of the expanded canal, a limited number of pilots experienced on the existing canal were certified for the transit of Neopanamax vessels by means of planned and innovative individual learning. After this organisational training through operative training, with the implementation of the expanded canal in June 2016, the routine training started. Hence the learning curve in the performance of these manoeuvres will represent the growing skill acquired by both the pilots and the organisation. Given that the learning effect is measurable, this paper has the dual objective of determining two curve models: the organisation operative learning curve model and the routine learning curve model for pilots performing transit manoeuvres in the expanded Panama Canal waterways and the Cocolí and Agua Clara locks. Manoeuvre times in locks and transit in the whole of the canal were followed up continuously in the first 42 months of operation.es_ES
dc.description.sponsorshipThe research of Salvador Naya and Javier Tarrío has been supported by MINECO grant MTM2017-82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema universitario de Galicia ED431G 2019/01), all of them through the ERDF. This work has been funded in part by Project 1-FACINA of the International Maritime University of Panama (UMIP).
dc.description.sponsorshipXunta de Galicia; ED431C-2020-14
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01
dc.description.sponsorshipUniversidad Marítima Internacional de Panamá; Project 1-FACINA
dc.language.isoenges_ES
dc.publisherCambridge University Presses_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION
dc.relationinfo:eu-repo/grantAgreement/Xunta de Galicia/2020/ED431C-2020-14/ES-GA
dc.relationinfo:eu-repo/grantAgreement/Xunta de Galicia/2019/ED431G 2019%01/ES-GA
dc.relation.urihttps://doi.org/10.1017/S0373463320000727es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCanal de Panamá - Navegaciónes_ES
dc.subjectBuques - Maniobrases_ES
dc.subjectExpanded Panama Canal
dc.subjectLearning effect
dc.subjectLearning curve
dc.subjectLockage time
dc.subjectTransit time
dc.subjectNon-linear regression
dc.subjectGeneralised additive models
dc.titleModelling Operative and Routine Learning Curves in Manoeuvres in Locks and in Transit in the Expanded Panama Canales_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleThe Journal of Navigationes_ES
UDC.volume74es_ES
UDC.issue3es_ES
UDC.startPage633es_ES
UDC.endPage655es_ES
dc.identifier.doi10.1017/S0373463320000727


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