{"id":"W4224273854","doi":"10.1080/07038992.2022.2059755","title":"Quantitative Inversion Modeling Method for Grading Deerni Copper Deposits Based on Visible and Near-Infrared Hyperspectral Data","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Hyperspectral imaging; Dimensionality reduction; Curse of dimensionality; Artificial neural network; Inversion (geology); Artificial intelligence; Computer science; Pattern recognition (psychology); Data set; Imaging spectrometer; Algorithm; Remote sensing; Data mining; Spectrometer; Geology; Optics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004960872,0.0006214938,0.0003072292,0.0007470021,0.0002711445,0.0005476585,0.0006828786,0.0003777526,0.0005838508],"category_scores_gemma":[0.0007861104,0.0002925533,0.0004697189,0.0004476757,0.0002696881,0.0008401118,0.0003658485,0.0005194442,0.0001765285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006446495,"about_ca_system_score_gemma":0.0009096409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009065093,"about_ca_topic_score_gemma":0.01016287,"domain_scores_codex":[0.9997798,0.00002297969,0.00001261339,0.00005413885,0.000114449,0.00001603831],"domain_scores_gemma":[0.9998301,0.00003255843,0.00003063393,0.00001410254,0.00008741863,0.000005182073],"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":[0.0001490748,0.0001649079,0.01068121,0.0001525853,0.00008424831,0.00008316577,0.0002236286,0.6804658,0.1043112,0.004036677,0.0008590976,0.1987885],"study_design_scores_gemma":[0.000004155108,0.00001574048,0.001149221,0.000003211149,0.00001109405,0.00001309737,0.00001518339,0.9868599,0.01111704,0.0004939861,0.0003079911,0.000009382289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09868647,0.00006290139,0.898901,0.00006313378,0.00001196815,0.00006247673,0.00008337531,0.0005691178,0.001559546],"genre_scores_gemma":[0.8147281,0.0001215426,0.1826101,0.00002754007,0.000006883133,0.0001165698,0.0001826887,0.00006694665,0.002139491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009065093,"threshold_uncertainty_score":0.01802468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06187705001994575,"score_gpt":0.3179457542832536,"score_spread":0.2560687042633078,"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."}}