ACCURACY ASSESSMENT Clause Samples
The Accuracy Assessment clause establishes a process for verifying the correctness and completeness of information or representations provided by one party to another. Typically, this clause allows the receiving party to review, test, or otherwise evaluate the data or statements to ensure they meet agreed-upon standards or requirements. By enabling such verification, the clause helps prevent misunderstandings or disputes arising from inaccurate or incomplete information, thereby promoting transparency and trust between the parties.
ACCURACY ASSESSMENT. Previous VA matches with the Social Security Administration indicate that the names and social security numbers (SSNs) in VA records are 99 percent accurate. VA internal verification procedures have also confirmed this percent of accuracy in VA records. BOP believes that virtually all of the names and SSNs that it will provide to VA will be the same as those furnished by the inmate sources.
ACCURACY ASSESSMENT. The SSA Enumeration System used for SSN matching is 100 percent accurate based on SSA’s Office of Analytics, Review, and Oversight (FY 2018 Enumeration Accuracy Review Report, April 2019). SSA does not have an accuracy assessment specific to the data elements listed in this Agreement. However, SSA conducts periodic, statistically valid, stewardship (payment accuracy) reviews, in which the benefits or payments listed in this Agreement are included as items available for review and correction. SSA quality reviewers interview the selected RSDI and SSI beneficiaries/recipients and redevelop the non-medical factors of eligibility to determine whether the payment was correct. Based on the available study results, we have a reasonable assurance that SSA’s accuracy assumptions of a 95 percent confidence level for the monthly benefits or payments listed in this Agreement. DHS-USCIS currently estimates that information within its CLAIMS 3 database is 90-95 percent accurate in reflecting immigration status, but continues to undertake various actions to further improve the quality of the CLAIMS 3 database. In addition, per standard operating procedures, USCIS adjudication officers conducting the queries may consult the USCIS Central Index System for additional information to correct errors. This process includes procedures for DHS-USCIS to correct any errors detected in the CLAIMS 3 immigration status information. ICE currently estimates that removal information recorded in the EID is 99 percent accurate. ICE continues to undertake various actions, such as maximizing automation, to further improve the quality of data submitted to the EID database and thus minimize human error that can occur during manual data entry. ICE law enforcement personnel conduct biometric validation and submit record checks against multiple systems, in addition to comprehensive interviews, to ensure that a subject’s identity is properly captured as part of the enforcement lifecycle.
ACCURACY ASSESSMENT. Individual employers and claimants report the information in the EDD’s files. Since the EDD is not the originator of the information disclosed, the EDD cannot guarantee the accuracy of the information. E00728 ▇▇▇▇ ▇▇▇▇▇▇ ▇▇▇ ▇▇▇ ▇▇▇▇▇▇, ▇▇▇ ▇▇▇▇▇ ▇▇▇▇▇▇, ▇▇ ▇▇▇▇▇ 707-268-2595 kbaxley@co.humboldt.ca.u s E00729 ▇▇▇▇▇▇ ▇▇▇▇▇▇▇ ▇▇▇ ▇▇▇ ▇▇▇▇▇▇, ▇▇▇ ▇▇▇▇▇ ▇▇▇▇▇▇, ▇▇ ▇▇▇▇▇ 707-268-2576 ▇▇▇▇▇▇▇▇@▇▇.▇▇▇▇▇▇▇▇.▇▇ .us E00735 ▇▇▇▇▇▇ ▇▇▇▇▇▇ ▇▇▇ ▇▇▇ ▇▇▇▇▇▇, ▇▇▇ ▇▇▇▇▇ ▇▇▇▇▇▇, ▇▇ ▇▇▇▇▇ 707-268-2578 ▇▇▇▇▇▇▇@▇▇.▇▇▇▇▇▇▇▇.▇▇. us
ACCURACY ASSESSMENT. An age model was not build for this project, so it is not possible to perform an accuracy assessment.
ACCURACY ASSESSMENT. Average crown diameter estimates are within plus or minus five feet of the actual mean crown diameter for over 60% of the sample plots – represented by the histogram in Figure 19. This result is the first major reflection of only using a single algorithm for tree identification; it may perform relatively well for many of the stand structures in the area, but outliers do exist and may introduce extraneous error if they are not specifically accounted for.
ACCURACY ASSESSMENT. Because stem mapping was not implemented in the project’s data collection component, there are no explicit trees that can be used as training objects for accuracy assessment. There were enough stands that were deciduous-dominant and coniferous-dominant to create a prediction that can gauge the two compositions, but without the exact position of any particular tree species it might not be possible to predict this level of granularity.
ACCURACY ASSESSMENT. − − In 40% of plots, tree stand density predictions are within +5 trees of actual plot counts. In almost 75% of plots, predictions are within +10 trees. Following the error in estimation of the other metrics, the tree delineation tends to underestimate the number of trees per acre.
ACCURACY ASSESSMENT. We did not collect data on where the true channel locations were, so there was no way to test the accuracy of any specific stream channel model or the set of processing choices used to create it, however, as the above figures show, LiDAR is very effective at capturing hydrological features on the landscape due to the detail ground models, this is beyond and above the abilities of other remote sensing technologies.
ACCURACY ASSESSMENT. Estimated Canopy Percent Cover was within ± 25% of field estimated canopy cover for 63 of the 113 plots [56% of all plots]. This outcome is similar to imagery predictions of crown diameter, which are strongly dependent on how individual trees are identified from imagery. As the TDA is configured to fit the majority of plot compositions [▇▇▇▇▇▇▇ Firs and Western Hemlocks] there are outliers that warrant a more dynamic approach. The field estimates of the canopy cover were estimated using a spherical densiometer. While this estimate is lacking some objective rigor that is associated with other metrics, it still provides an insight as to how well imagery algorithms are classifying tree canopy area per plot. Imagery predictions were generally underestimating the canopy percent cover when compared to field data. This follows in suit with crown diameter estimations, which were also underestimated more often than overestimated.
ACCURACY ASSESSMENT. A canopy percent cover model was not build for this project, so it is not possible to perform an accuracy assessment. Again, we avoided building this model so as not to under represent the utility of LiDAR data by comparing it to unsuitable field data. Previous work has shown that LiDAR outperforms imagery in canopy percent cover estimates as documented in our past literature review pilot (▇▇▇▇▇▇ & ▇▇▇▇▇ 2015.)