dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to make any assessment of consistency objective, a study needs to specify the threshold used. This is shown for the following examples for various metrics. In essence, consistency then means that several datasets have been evaluated against the underlying physical background knowledge and were found "fit for purpose" for a specific application domain. This leads to cases where seemingly small values of a chosen metric (compared to its uncertainty) can mean inconsistency, whilst in other cases apparently large deviations mean consistency, as will be shown in the examples of this section. This means that testing retrieval level consistency needs to assess auxiliary dataset differences in the light of target variable sensitivities or incompatibilities.
Consistency of categorical auxiliary datasets ("masks") A first approach to assess consistency of masks used in independent retrievals lies in visual inspection of combined maps of datasets, as for example, of surface temperature composed from four independent CDRs for land (LST), sea surface (SST), ice (IST) and lake surface water (LSWT) temperatures against required pixel-level agreement of the masks. In CCI the four retrievals use a common land-sea mask (and sea-ice mask), but apply different cloud mask algorithms optimized over land, sea ice and water surfaces. As shown in Figure  3 , the reader can visually confirm the absence of any obvious scatter near the land-sea borders, which indicates that the land-sea masks used in the different processing systems are consistent. Additionally, the application of different optimal cloud masking in the retrievals for LST and SST has led to obvious discontinuities in the sampling with temperature observations at the land-sea border, which may be judged as second-order inconsistencies.
g. Sogacheva et al. , 2017; Li et al. , 2009). To assess if this principle is fulfilled, independent AATSR cloud masks used in the aerosol and cloud products were analysed for four days in September 2008 (covering difficult scenes with high aerosol loads or complicated mixtures of aerosol and clouds). locations between the outlines of physically related quantities (different products within one ECV, between different ECVs). For example, glacier outlines are derived from high-resolution satellite imagery or aerial photography using semi-automated mapping techniques or manual on-screen digitization  (Paul et al. , 2015). Due to their higher spatial resolution, the location of glaciers can be used for land cover as an independent validation source for its "permanent ice and snow" classes. Furthermore, glacier maps serve as an important auxiliary dataset for clouds and LST (to choose the correct retrieval algorithm), and lakes as a reciprocal mask (these can only occur in places not covered by glaciers) for sea ice, ice sheets and permafrost.