{"id":"W4393360410","doi":"10.1007/978-981-97-1316-5_4","title":"Remote Sensing and Geographic Information Systems Driven Data Analysis","year":2024,"lang":"en","type":"book-chapter","venue":"Water science and technology library","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Geographic information system; Remote sensing; Geography; Computer science; Data science; Cartography","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":[0.0002555003,0.0002547599,0.0002920096,0.001067598,0.0002728688,0.0005363808,0.0007277913,0.0004331933,0.00002851869],"category_scores_gemma":[0.00001014214,0.0001601349,0.00002990524,0.0008724402,0.002234586,0.002257537,0.003400032,0.0004255246,0.0002032526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003032085,"about_ca_system_score_gemma":0.00001817234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008778603,"about_ca_topic_score_gemma":0.00001997278,"domain_scores_codex":[0.9982066,0.000006259922,0.0002739546,0.000765513,0.0004306294,0.0003170512],"domain_scores_gemma":[0.9988584,0.0000105386,0.00009421313,0.0009287043,0.00001683795,0.00009129005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002438895,0.00001845508,0.0091286,0.0006085483,0.001694808,0.0008514631,0.002393434,0.0003978389,0.01388426,0.06369939,0.02320119,0.8840976],"study_design_scores_gemma":[0.0001367217,0.00007995193,0.0008859468,0.0003468115,0.0008924648,0.00068131,0.0001727604,0.1763241,0.0008307038,0.04906011,0.7695498,0.001039393],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05054258,0.002395486,0.0007851606,0.0130335,0.001135189,0.001207151,0.0001853569,0.002047749,0.9286678],"genre_scores_gemma":[0.7270291,0.004158893,0.02982208,0.001176539,0.0002743152,8.359074e-7,0.001541543,0.0001347465,0.2358619],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8830582,"threshold_uncertainty_score":0.8233426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006368310464209316,"score_gpt":0.1770325857709141,"score_spread":0.1706642753067048,"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."}}