{"id":"W2769088119","doi":"10.3390/ijgi6120387","title":"Machine Learning Techniques for Modelling Short Term Land-Use Change","year":2017,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja; Compute Canada","keywords":"Support vector machine; Geospatial analysis; Ranking (information retrieval); Term (time); Machine learning; Computer science; Representation (politics); Artificial neural network; Artificial intelligence; Data mining; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003891675,0.00008263231,0.0001096634,0.00008503688,0.0002325732,0.0004072093,0.0004868929,0.00005179571,0.00009652389],"category_scores_gemma":[0.00004465473,0.00006318654,0.00008139802,0.00001530716,0.00001015009,0.007492126,0.0001058353,0.0001111945,0.00004453118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009428152,"about_ca_system_score_gemma":0.000006419023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000490706,"about_ca_topic_score_gemma":0.00009286954,"domain_scores_codex":[0.9991043,0.000009912323,0.0003644519,0.00004834835,0.000359365,0.0001136733],"domain_scores_gemma":[0.9991922,0.00002515121,0.000519273,0.0001022557,0.0001114914,0.00004965271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003617441,0.00005140459,0.8118331,0.00004952278,0.0001262189,0.000009397432,0.001862536,0.01415459,0.0001759016,0.0001604009,0.0004161933,0.170799],"study_design_scores_gemma":[0.001480363,0.0003477964,0.09272786,0.0004203221,0.00007183029,0.0002110697,0.0001264297,0.5444779,0.00438684,0.0008934248,0.3544025,0.0004536661],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978286,0.00003398394,0.09594791,0.001353935,0.001329798,0.000372517,0.00007675165,0.00004433085,0.003012134],"genre_scores_gemma":[0.9959989,0.0002015374,0.003219316,0.0002156328,0.0002789377,0.00001112964,0.00004634133,0.00000484047,0.00002331645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7191052,"threshold_uncertainty_score":0.543161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02955549204331127,"score_gpt":0.2743111776975799,"score_spread":0.2447556856542686,"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."}}