{"id":"W7119350833","doi":"","title":"MULTISPECTRAL DIGITAL DATA ANALISYS OF HIGH RESOLUTION ACQUIRED WITH THE COMPACT AIRBORNE SPECTROGRAPHIC IMAGER SENSOR IN THE COUNTRY AREA OF PARANÁ STATE - BRAZIL","year":2015,"lang":"pt","type":"article","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multispectral image; Spectral bands; Thematic map; Multispectral pattern recognition; High resolution; Stereoscopy; Digital image; Resolution (logic); Image resolution","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002123817,0.0001520768,0.0001111917,0.001503799,0.000234864,0.0003112808,0.0001776001,0.0001285626,0.0006923444],"category_scores_gemma":[0.000502197,0.00009976484,0.000156359,0.001848652,0.00020305,0.0002448799,0.0002740138,0.00008731578,0.0001280987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005470904,"about_ca_system_score_gemma":0.0005073884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07994378,"about_ca_topic_score_gemma":0.1985391,"domain_scores_codex":[0.999744,0.00003135003,0.00001318957,0.00005657162,0.0001305888,0.00002422804],"domain_scores_gemma":[0.9997826,0.00003501402,0.00003617876,0.00002906637,0.0001070209,0.00001006373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001361686,0.0001223865,0.6450055,0.0005202548,0.0001376951,0.001204789,0.004978453,0.004992436,0.1024584,0.002848427,0.002359315,0.2352361],"study_design_scores_gemma":[0.000003289188,0.0000267344,0.9829266,0.00002657988,0.00003163798,0.0004372409,0.002346277,0.003010293,0.004381208,0.0001870486,0.006609458,0.0000135835],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853724,0.0002649334,0.003286772,0.00005873644,0.000004883249,0.00004494323,0.001711946,0.00006307621,0.009192267],"genre_scores_gemma":[0.9927354,0.0001426242,0.005414165,0.00001273732,0.000002273187,0.00002363548,0.0006915323,0.00001051138,0.0009671636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07994378,"threshold_uncertainty_score":0.1589569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02502238293402313,"score_gpt":0.2417356566539609,"score_spread":0.2167132737199378,"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."}}