{"id":"W2907969535","doi":"10.1007/s10661-018-7152-y","title":"A unified framework for land cover monitoring based on a discrete global sampling grid (GSG)","year":2019,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"Deutsche Forschungsgemeinschaft","keywords":"Sampling (signal processing); Cover (algebra); Environmental science; Grid; Land cover; Ecotoxicology; Environmental resource management; Land use; Computer science; Geology; Engineering; Civil engineering; Ecology; Biology; Geodesy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001669706,0.0002356585,0.0001872399,0.00001988033,0.0002792535,0.00009448377,0.0001361631,0.00009814976,0.00009647923],"category_scores_gemma":[0.000008241392,0.0002210164,0.00007804205,0.00008250081,0.0000747271,0.00009932122,0.0001063063,0.0002171494,0.0001383651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004975817,"about_ca_system_score_gemma":0.000009300623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000686885,"about_ca_topic_score_gemma":3.401084e-7,"domain_scores_codex":[0.9985075,0.00003436278,0.0001978926,0.0005327218,0.0003769907,0.0003505075],"domain_scores_gemma":[0.9992175,0.0001727153,0.00008906655,0.0003654862,0.000001458792,0.0001537639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004346135,0.0001216966,0.9582319,0.0000140048,0.00001893216,0.000001388472,0.00005719878,0.01985954,0.006257406,0.0001202538,0.00002243176,0.01525175],"study_design_scores_gemma":[0.0008723737,0.0002828148,0.9747368,0.0001601611,0.00003770853,0.000003286025,0.0002593174,0.009574519,0.003108402,0.0008318441,0.009712848,0.0004199714],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771635,0.00003693238,0.01824046,0.0002049942,0.0008099108,0.000542675,0.00005749928,0.00005919677,0.002884821],"genre_scores_gemma":[0.9342952,0.00003129942,0.06501106,0.00003405338,0.0003422793,0.00003116602,0.00002114163,0.0000255466,0.0002082707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0467706,"threshold_uncertainty_score":0.9012789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568017894941585,"score_gpt":0.2983323312455894,"score_spread":0.2826521522961736,"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."}}