{"id":"W2982053709","doi":"10.4095/219855","title":"Application of Hyperspectral Remote Sensing for LAI Estimation in Precision Farming","year":2001,"lang":"en","type":"report","venue":"","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Precision agriculture; Estimation; Environmental science; Agriculture; Computer science; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000733508,0.0001598782,0.0003365995,0.0002267801,0.00005404644,0.00002730137,0.00007749855,0.0002336009,0.00003588436],"category_scores_gemma":[0.0002239063,0.0001283803,0.00009444417,0.0002124654,0.00002218587,0.00006628145,0.000003944296,0.0001530285,0.00001037083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002510836,"about_ca_system_score_gemma":0.0001779373,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03923417,"about_ca_topic_score_gemma":0.01211091,"domain_scores_codex":[0.9986069,0.00003467462,0.0004457361,0.0003187015,0.0003784585,0.000215485],"domain_scores_gemma":[0.9990836,0.0002277912,0.0002625932,0.0002245172,0.0001551438,0.00004632562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003100157,0.000003740418,0.001456813,0.00007624504,0.000007085579,0.000003258015,0.00005679234,0.006886281,0.0000263971,0.000001200565,0.0002914499,0.9911597],"study_design_scores_gemma":[0.0002051433,0.00006218263,0.01439905,0.0002120072,0.0000293775,0.00008046654,0.00005454377,0.9672993,0.0001578556,0.0009308415,0.01638401,0.000185211],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1569469,0.001157922,0.5760952,0.0002008966,0.001035502,0.00174639,0.00007138545,0.0001425213,0.2626033],"genre_scores_gemma":[0.7134475,0.00143055,0.2787801,0.00004093432,0.000480715,8.477927e-8,0.001727403,0.00002200165,0.004070673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9909745,"threshold_uncertainty_score":0.9671637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03153163607116227,"score_gpt":0.2874242434313272,"score_spread":0.2558926073601649,"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."}}