Search results for "error"

The study aims at assessing the differential effects of face-to-face paper and computer assisted interviewing in terms of efficacy, efficiency and the production of non-sampling errors. The research ground is constituted by the analysis of the customer satisfaction of a large-scale retail trade group owning many shops in Sicily. The reaction of respondents to paper versus electronic questionnaires is investigated by means of an experimental design in terms of non sampling error amounts.

CAPIPAPISettore SECS-S/05 - Statistica SocialeNON-SAMPLING ERRORS

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Near-infrared diffuse reflectance spectroscopy and neural networks for measuring nutritional parameters in chocolate samples.

2007

Abstract A rapid and non-destructive method has been developed for the characterization of chocolate samples based on diffuse reflectance near-infrared Fourier transform spectroscopy (DRIFTS) and artificial neural networks (ANNs). This methodology provides a potentially useful alternative to time-consuming chemical methods of analysis. To assess its utility, 36 chocolate samples purchased from the Spanish market were analyzed for the determination of the main nutritional parameters like carbohydrates, fat, proteins, energetic value and cocoa content. Direct triplicate measurements of each sample were carried out by DRIFTS. Cluster hierarchical analysis was used for selecting calibration and…

CacaoDiffuse reflectance infrared fourier transformMean squared errorChemistryFood HandlingNear-infrared spectroscopyAnalytical chemistryCarbohydratesInfrared spectroscopyCalorimetryBiochemistryStandard deviationFourier transform spectroscopyAnalytical ChemistryMilkNutrition AssessmentApproximation errorSpectroscopy Fourier Transform InfraredCalibrationEnvironmental ChemistryAnimalsNerve NetSpectroscopyAnalytica chimica acta

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Experimental conditions for respiration and growth studies of F0 and F1 larval and juvenile European seabass Dicentrarchus labrax

2022

Water parameters in the 2 years before spawning of F0 (08.02.2016-06.03.2018) and during larval and juvenile phase of F1: Larval period until 17.05.2018 (48 dph, 900 dd) and 01.06.2018 (63 dph, ~900 dd) for warm and cold life condition respectively, for the juveniles until 28.09.2018 (180 dph, ~4000 dd) and 12.02.2019 (319 dph, ~5100 dd) for warm and cold conditioned fish respectively. Means ± s.e. over all replicate tanks per condition. Temperature (Temp.), pH (free scale), salinity, oxygen and total alkalinity (TA) were measured weekly in F1 and monthly in F0; sea water (SW) measurements were conducted in 2017 and 2018. Water parameters during larval and early juvenile phase of F0: Larval…

Calculated by CO2sys_xls_program Lewis and WallaceSalinityAlkalinityLife stageType of studyTemperature waterGermanyCalculatedAlkalinity totalSalinity standard errortotallarval growthteleostexperiment2006pH2007 with purified m cresol purpleOcean acidificationMeasured spectrophotometrically Dickson et alTemperatureWeilheimPartial pressure of carbon dioxide (water) at sea surface temperature (wet air) standard errorSilicatedissolvedLaboratory experimentSEAL AA3 segmented flow autoanalyzerSilicate standard errorstandard errorMultiprobeEarth System ResearchWTW 340imetabolic rateswaterGenerationOxygen dissolvedMultiprobe WTW 340iPhosphateocean warmingAlkalinity total standard errorjuvenile growthCarbon dioxide (water) partial pressurepH meter (WTW 3110) with electrode (WTW Sentix 41)DATE TIMEWTW Oxi 340i probeTemperature water standard errorCarbon dioxide water partial pressurepH standard errorXylem Analytics GermanySalinometer (WTW LF325 Xylem Analytics Germany Weilheim Germany)Calculated by CO2sys_xls_program (Lewis and Wallace 2006)OxygenTreatmentDATE/TIMEPartial pressure of carbon dioxide water at sea surface temperature wet airSalinometer WTW LF325Oxygen dissolved standard errorMeasured spectrophotometrically (Dickson et al. 2007) with purified m-cresol purplepH meter WTW 3110 with electrode WTW Sentix 41Phosphate standard error

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Genome-wide meta-analyses of multiancestry cohorts identify multiple new susceptibility loci for refractive error and myopia

2013

Author version made available in accordance with the publisher's policy.

Candidate geneRefractive errorBone Morphogenetic Protein 2Genome-wide association studyVARIANTSGenomeGenome-wide association studies0302 clinical medicineRisk FactorsMyopiaGRIA4Genetics0303 health sciencesKCNQ Potassium ChannelsDisease geneticsEYE GROWTHASSOCIATIONRETINAL-PIGMENT EPITHELIUMRefractive ErrorsGenetic load3. Good healthADAPTED MOUSE RETINAMeta-analysisACIDPOTASSIUM CHANNELEXPRESSIONSingle-nucleotide polymorphismBiologyWhite PeopleArticle03 medical and health sciencesAsian PeoplemedicineGeneticsHumansGenetic Predisposition to DiseaseReceptors AMPAgene; myopia; refractive030304 developmental biologyHomeodomain Proteinsta1184ta3121medicine.diseaseGENEAlcohol OxidoreductasesSERINE-PROTEASEbiology.protein030221 ophthalmology & optometrySusceptibility locusTrans-ActivatorsEye disorderLamininSerine ProteasesGWAS; meta-analyses; refractive error; myopiaGenome-Wide Association StudyNature Genetics

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A global Canopy Water Content product from AVHRR/Metop

2020

Abstract Spatially and temporally explicit canopy water content (CWC) data are important for monitoring vegetation status, and constitute essential information for studying ecosystem-climate interactions. Despite many efforts there is currently no operational CWC product available to users. In the context of the Satellite Application Facility for Land Surface Analysis (LSA-SAF), we have developed an algorithm to produce a global dataset of CWC based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board Meteorological–Operational (MetOp) satellites forming the EUMETSAT Polar System (EPS). CWC reflects the water conditions at the leaf level and information related …

Canopy010504 meteorology & atmospheric sciencesMean squared errorAdvanced very-high-resolution radiometerCanopy Water Content (CWC)0211 other engineering and technologiesGaussian Process Regression (GPR)FOS: Physical sciencesContext (language use)02 engineering and technologyAVHRR/MetOp01 natural sciencesComputers in Earth SciencesEngineering (miscellaneous)Water content021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingVegetation15. Life on landAtomic and Molecular Physics and OpticsComputer Science ApplicationsPhysics - Atmospheric and Oceanic PhysicsMODIS13. Climate actionEUMETSAT Polar System (EPS)Atmospheric and Oceanic Physics (physics.ao-ph)Spatial ecologyEnvironmental scienceSatelliteSentinel-2

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Canopy chlorophyll content and LAI estimation from Sentine1-2: Vegetation indices and Sentine1-2 Leve1-2A automatic products comparison

2019

The aim of this work is to analyze different methodologies for the estimation of leaf area index (LAI) and canopy chlorophyll content (CCC), using the Sentine1-2 satellite. LAI and CCC are biophysical parameters indicator of crop health state and fundamental in the productivity prediction. The purpose is to define the most optimal LAI and CCC estimation method for operational use in the monitoring of agricultural areas. Moreover, the CCC and LAI automatic products obtained directly through the Sentinel Application Platform Software (SNAP) biophysical processor and Sentine1-2 images by means of an artificial neural network (ANN) are validated. On the other hand, common vegetation indices use…

CanopyDiscrete mathematicsvalidationChlorophyll contentMean squared errorcanopy chlorophyll contentState (functional analysis)VegetationLAIvegetation indicesSaturation (graph theory)Leaf area indexSentinel-2Mathematics

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