{"id":"W4393498829","doi":"10.5281/zenodo.7348972","title":"Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Subarctic climate; Watershed; George (robot); Scale (ratio); Hydrology (agriculture); Identification (biology); Cluster analysis; Environmental science; Geography; Physical geography; Ecology; Computer science; Cartography; Geology; Archaeology; Artificial intelligence; Biology; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00123132,0.001117601,0.0007203686,0.004231196,0.001498534,0.001292665,0.002551211,0.0008609536,0.006921063],"category_scores_gemma":[0.003577218,0.0004720881,0.0008475435,0.005874173,0.0004940237,0.0005397525,0.001698058,0.001011499,0.004921144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007120761,"about_ca_system_score_gemma":0.01077296,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9221909,"about_ca_topic_score_gemma":0.9658869,"domain_scores_codex":[0.999156,0.00006806324,0.0000621929,0.0002397646,0.0002657916,0.0002081941],"domain_scores_gemma":[0.9976319,0.0001971291,0.000146869,0.0003353673,0.001430624,0.0002581547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002952687,0.0002546346,0.07837115,0.0007656065,0.0002679133,0.0002537034,0.0004242929,0.00622053,0.002725049,0.001510147,0.8366585,0.07225315],"study_design_scores_gemma":[0.0003583855,0.000059714,0.4906388,0.0005773669,0.0001608548,0.0001796427,0.002669068,0.04581974,0.00388196,0.002692163,0.4527874,0.0001749138],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01777041,0.0002858802,0.002743663,0.0003077213,0.00007488767,0.0003204874,0.9731967,0.002762791,0.002537612],"genre_scores_gemma":[0.02671545,0.0001119136,0.01150367,0.00005856061,0.00001138766,0.0003786063,0.9589723,0.0002131047,0.002035064],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0778091,"threshold_uncertainty_score":0.1565346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748548138305469,"score_gpt":0.2247880496830326,"score_spread":0.207302568299978,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}