{"id":"W2358704408","doi":"","title":"Image classification for giant panda habitat using tasseled cap and matched filtering methods","year":2015,"lang":"en","type":"article","venue":"Zhongguo Kexueyuan Daxue xuebao","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Remote sensing; Decision tree; Computer science; Environmental science; Contextual image classification; Random forest; Artificial intelligence; Brightness; Classifier (UML); Pattern recognition (psychology); Image (mathematics); Geology","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":[0.0004740784,0.0004799053,0.0003704408,0.002176505,0.0003848842,0.0006315347,0.000358127,0.0004175496,0.000998111],"category_scores_gemma":[0.0005212732,0.0001668301,0.000634536,0.0009214947,0.0002469984,0.0007524054,0.0002810592,0.0002138515,0.0003257171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002868344,"about_ca_system_score_gemma":0.0003207034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005866823,"about_ca_topic_score_gemma":0.006972688,"domain_scores_codex":[0.9997038,0.00003126614,0.00002012176,0.00009400881,0.0001027068,0.00004802888],"domain_scores_gemma":[0.9997826,0.00003782952,0.00003385665,0.00001930386,0.0001098783,0.00001651027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000300839,0.00023727,0.05123074,0.0001623592,0.000161391,0.0003431376,0.0002511423,0.01879909,0.1095473,0.001428753,0.002008366,0.8155297],"study_design_scores_gemma":[0.0000223963,0.0001721161,0.1092018,0.0000262336,0.0001783211,0.0003755093,0.0003108904,0.8374122,0.04851402,0.001092683,0.002641246,0.00005264374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4408921,0.0003900182,0.552336,0.0001184889,0.00007425463,0.0001284735,0.0002760189,0.001882577,0.003902145],"genre_scores_gemma":[0.8085046,0.0001783905,0.1888638,0.00005513716,0.00002974849,0.00006068923,0.0004357632,0.00004369923,0.001828135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005866823,"threshold_uncertainty_score":0.01166534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116343661693636,"score_gpt":0.3367940098541096,"score_spread":0.225159643684746,"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."}}