{"id":"W2147928240","doi":"10.1109/igarss.2003.1293754","title":"Compressed hyperspectral imagery for forestry","year":2004,"lang":"en","type":"article","venue":"","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency; University of Victoria; Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Hyperspectral imaging; Uncompressed video; Computer science; Vector quantization; Data compression; Quantization (signal processing); Remote sensing; Artificial intelligence; Data mining; Algorithm; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003675057,0.00004793474,0.00004760235,0.000006489037,0.00006026423,0.00001629997,0.00007090013,0.00001699967,0.0004791741],"category_scores_gemma":[0.00001784844,0.00004193899,0.00002659853,0.00003615366,0.00005073932,0.00005174411,0.00003452662,0.00002516481,0.0001127115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004230937,"about_ca_system_score_gemma":0.000004968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003319786,"about_ca_topic_score_gemma":0.00004401498,"domain_scores_codex":[0.9995949,0.000001778721,0.0000610412,0.0001180957,0.00006951671,0.0001546564],"domain_scores_gemma":[0.9998302,0.00002620816,0.00001363891,0.00008327818,0.000002556777,0.00004409223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001721324,0.0008969762,0.06346221,0.00009880064,0.00009173255,0.00009123935,0.002008651,0.08629286,0.2232258,0.4257108,0.1405711,0.05737764],"study_design_scores_gemma":[0.005736011,0.0003803383,0.3575478,0.00003180221,0.00005148884,0.00004193441,0.0009491019,0.01551624,0.06022708,0.48329,0.07510696,0.001121268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5256581,0.00001488333,0.3342068,0.001088283,0.0002070011,0.0003436308,0.0000266097,0.0001044536,0.1383502],"genre_scores_gemma":[0.9145768,0.000002076954,0.0841852,0.0003789456,0.00003009213,0.00001504151,0.000006821966,0.000006250988,0.000798726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3889188,"threshold_uncertainty_score":0.524662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338041972239642,"score_gpt":0.2343940823192977,"score_spread":0.2210136625969013,"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."}}