https://w3id.org/sciencelive/np/RA45t1bdfz6Jr40G9dDqrWAF4i7-DT3HQNOSheRWjcuho
.trig | .trig.txt | .jelly | .jelly.txt | .jsonld | .jsonld.txt | .nq | .nq.txt | .xml | .xml.txt
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sub:assertion {
sub:delouis-2022-sst-replication-study a <https://w3id.org/sciencelive/o/terms/FORRT-Replication-Study>,
<https://w3id.org/sciencelive/o/terms/Reproduction-Replication-Study>;
<http://www.w3.org/2000/01/rdf-schema#label> "Scattering-transform SST gap-filling on Copernicus Marine data (Delouis 2022 + IGARSS 2024 tutorial replication)";
<http://www.w3.org/2004/02/skos/core#related> <http://www.wikidata.org/entity/Q1507383>,
<http://www.wikidata.org/entity/Q199687>, <http://www.wikidata.org/entity/Q5629401>,
<http://www.wikidata.org/entity/Q830457>;
<https://w3id.org/sciencelive/o/terms/hasDeviationDescription> "Differences from Delouis et al. 2022 (paper): (1) input data is Copernicus Marine SST in oceanography, not Planck dust polarisation in astrophysics; (2) the application is gap-filling clouds in remote-sensing imagery, not separating dust signal from instrument noise; (3) the resolution is nside=32 rather than the paper's nside=256, so the workflow runs on a CPU rather than requiring GPU. Differences from Jean-Marc Delouis's IGARSS 2024 tutorial notebook (which is the more direct source): we use the current PMW L3S product (0.25°) matched to the L4 product's grid; the original notebook may have used a different L3S variant. Same FOSCAT software — at the time of the experiment we used the annefou/FOSCAT@v0.1.0-cpu fork to enable CPU execution. The CPU patch (jmdelouis/FOSCAT#40) has since been merged upstream and is included in FOSCAT 2026.4.1 on PyPI (released 2026-04-24), so future runs of this workflow can use the standard PyPI release.";
<https://w3id.org/sciencelive/o/terms/hasDiscipline> <http://www.wikidata.org/entity/Q43518>;
<https://w3id.org/sciencelive/o/terms/hasMethodologyDescription> "We follow the workflow established by Jean-Marc Delouis in the IGARSS 2024 Pangeo tutorial (10.5281/zenodo.19793350). Inputs are Copernicus Marine L3S PMW SST (cmems_obs-sst_glo_phy_l3s_pmw_P1D-m, with cloud gaps) and L4 SST analysis (cmems_obs-sst_glo_phy-temp_nrt_P1D-m, gap-free reference) for 2026-04-01, both at 0.25° native resolution. Pipeline: (a) quality-filter L3S, regrid to L4 grid, build ocean mask from L4 NaN; (b) convert both to HEALPix at nside=32 via averaging; (c) fit a spherical-harmonic baseline (lmax=30) on observed pixels for an initial guess; (d) run FOSCAT scattering synthesis with scat_cov.funct(NORIENT=4, KERNELSZ=3) and L-BFGS over 300 epochs, using the L4 map's scattering coefficients as the statistical target and a cloud-only mask for gradient updates; (e) validate FOSCAT's filled values against L4 in cloudy regions via RMSE, comparing to the harmonic baseline. Inference runs on CPU (Apple M1 Pro, ~139 seconds).";
<https://w3id.org/sciencelive/o/terms/hasScopeDescription> "This study tests Delouis et al. 2022's generalisation claim on a different domain than the paper's Planck dust polarisation: the framework is applied to operational Copernicus Marine sea surface temperature observations to fill cloud gaps. We evaluate whether scattering-transform synthesis with a gap-free L4 reference product as the statistical target produces gap-filled L3S maps whose values approach the L4 ground truth in cloudy regions, and whether this outperforms a standard spherical-harmonic interpolation baseline.";
<https://w3id.org/sciencelive/o/terms/targetsClaim> <https://w3id.org/sciencelive/np/RAQPvE7Y4PNeL2oDwFh_uJgJbFHyBmWEvyZfO-RWy1pP8/delouis-2022-sst-cross-domain-claim> .
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<https://orcid.org/0000-0002-1784-2920> <http://xmlns.com/foaf/0.1/name> "Anne Fouilloux" .
this: dc:created "2026-04-26T16:16:03.211Z"^^xsd:dateTime;
dc:creator <https://orcid.org/0000-0002-1784-2920>;
dc:license <https://creativecommons.org/licenses/by/4.0/>;
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