Uncertainty assessments Sample Clauses

Uncertainty assessments. The WISER lake macrophyte data was used to examine variability associated with the varying levels of the hierarchical sampling scheme: transects within stations within waterbodies within countries. We assessed this for several response metrics, including: • Lake Macrophyte Intercalibration Metric for lake macrophytes (ICM-LM), • modified ▇▇▇▇▇▇▇▇▇ score, • maximum growing depth (C_max), • species richness. Each metric was calculated for each transect. We used lake level alkalinity and total phosphorus concentration as covariates in the analyses for two reasons. Firstly, these variables define strong gradients in the dataset. Secondly, the response of the macrophyte metrics to lake- level TP, accounting for variations in alkalinity, and for uncertainty in surveying lakes, is of considerable interest in itself. As these covariates were measured at the waterbody level they explain variance between waterbodies and countries, but not within waterbodies. We examined correlation between metrics at the various levels in the sampling hierarchy, and correlations between explanatory variables. Alkalinity information for Étang des Aulnes (France) was not available. Data from this lake were excluded from further analyses. We undertook the uncertainty analyses using linear mixed effects models, fitted using the nlme package in the R environment for statistical computing (R Development Core Team, 2008). The levels of the sampling hierarchy were specified as nested random effects, with the lowest level, variation between transects, forming the residual. We used TP and alkalinity, measured at the lake level, as explanatory variables in all analyses because of their strong relationship to the metrics studied. Depending on the focus of the analysis, we used additional explanatory variables. For example when looking at metric values for different depth zones, an indicator variable for depth zone was used. TP and alkalinity were log transformed, and richness was square root transformed prior to model fitting. Correlations between metrics were examined by first creating a data matrix with a single column for metric value and second column containing an indicator variable for the metric name. Analysis then considered the responses of pairs of metrics modelled as a bivariate normal distribution which can consider correlations between metrics at each of the levels of the sampling hierarchy. Models were fitted using Residual Maximum Likelihood Estimation (REML) to produce unbiased ...

Related to Uncertainty assessments

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  • Impact Assessments 5.1 The Parties shall: (a) provide all reasonable assistance to the each other to prepare any data protection impact assessment as may be required (including provision of detailed information and assessments in relation to Processing operations, risks and measures); and (b) maintain full and complete records of all Processing carried out in respect of the Personal Data in connection with the contract, in accordance with the terms of Article 30 GDPR.

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  • Security Assessment If Accenture reasonably determines, or in good faith believes, that Supplier’s security practices or procedures do not meet Supplier’s obligations under the Agreement, then Accenture will notify Supplier of the deficiencies. Supplier will without unreasonable delay: (i) correct such deficiencies at its own expense; (ii) permit Accenture, or its duly authorized representatives, to assess Supplier’s security-related activities that are relevant to the Agreement; and (iii) timely complete a security questionnaire from Accenture on a periodic basis upon Accenture’s request. Security issues identified by Accenture will be assigned risk ratings and an agreed-to timeframe to remediate. Supplier will remediate all the security issues identified within the agreed to timeframes. Upon Supplier’s failure to remediate any high or medium rated security issues within the stated timeframes, Accenture may terminate the Agreement in accordance with Section 8 above.