{"id":"W2240887693","doi":"10.1007/978-94-007-6190-2_17","title":"A Data Mining Approach to Recognize Objects in Satellite Images to Predict Natural Resources","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Satellite; Artificial intelligence; Pixel; Object (grammar); Computer vision; Identification (biology); Process (computing); Pattern recognition (psychology); Remote sensing; Satellite image; Geography; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006615386,0.000681921,0.0006801808,0.002113769,0.000601458,0.001037092,0.001523944,0.0006944149,0.001255034],"category_scores_gemma":[0.001313758,0.0003174247,0.001459154,0.002717279,0.0003938389,0.001080436,0.0006845366,0.0008241142,0.0008019903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005446826,"about_ca_system_score_gemma":0.0007228336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006412349,"about_ca_topic_score_gemma":0.007687346,"domain_scores_codex":[0.9995837,0.0000422817,0.00006106439,0.0001289655,0.0001607975,0.00002337513],"domain_scores_gemma":[0.9994676,0.0002325204,0.00003862701,0.00006552254,0.0001724343,0.00002340335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001493028,0.0005699486,0.008902395,0.0003319936,0.0003404164,0.0003660251,0.0002227491,0.03729056,0.01538101,0.00664226,0.008782097,0.9210212],"study_design_scores_gemma":[0.00003759597,0.0002425015,0.01154669,0.0001197873,0.0002912547,0.0007933494,0.0003306915,0.9067446,0.02559509,0.03080342,0.02343844,0.00005663233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04004497,0.001500856,0.9497254,0.0006871931,0.0001727838,0.0003020193,0.002196993,0.001544748,0.003825096],"genre_scores_gemma":[0.1412946,0.001051416,0.8499709,0.0002008615,0.000110506,0.000272797,0.003025051,0.00005464719,0.004019245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006412349,"threshold_uncertainty_score":0.01275009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01815939897665524,"score_gpt":0.2284764470470582,"score_spread":0.2103170480704029,"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."}}