{"id":"W4403336786","doi":"10.3390/rs16203778","title":"Predicting Rock Hardness and Abrasivity Using Hyperspectral Imaging Data and Random Forest Regressor Model","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperspectral imaging; Random forest; Soil science; Geology; Remote sensing; Environmental science; Artificial intelligence; Computer science","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":[0.0009133784,0.0009515302,0.0006244596,0.0009939191,0.0001770783,0.0005538753,0.0006733622,0.0005804169,0.0005486904],"category_scores_gemma":[0.001563634,0.0003108013,0.0008649152,0.0006961938,0.00025376,0.0008147031,0.0002793787,0.000715722,0.0003884342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003364274,"about_ca_system_score_gemma":0.0005047957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009378297,"about_ca_topic_score_gemma":0.009738701,"domain_scores_codex":[0.9996753,0.00005673467,0.00001531751,0.000116988,0.00009482315,0.00004073457],"domain_scores_gemma":[0.9994581,0.0002541955,0.00009501997,0.00004047386,0.0001340042,0.00001811669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001462834,0.000201293,0.01746641,0.00009126993,0.0001259692,0.0001080799,0.00005424662,0.8609662,0.01154879,0.0006987119,0.0006876849,0.1079051],"study_design_scores_gemma":[0.00000212559,0.00001603486,0.002134057,0.000002774691,0.000007974983,0.0000120192,0.000005143186,0.9967946,0.000773937,0.000149311,0.00009587852,0.000005962394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3166435,0.0005542858,0.6795201,0.0001082237,0.00004054095,0.00007183565,0.0004308799,0.001317909,0.001312667],"genre_scores_gemma":[0.9140618,0.0003353711,0.08293106,0.00003838906,0.00003400064,0.00007944218,0.0009446898,0.00005984304,0.001515408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009378297,"threshold_uncertainty_score":0.01864737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011644089448587,"score_gpt":0.2670234201595636,"score_spread":0.2369069792650777,"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."}}