{"id":"W1495377732","doi":"10.1109/igarss.2004.1369826","title":"Forest information from hyperspectral sensing","year":2004,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; York University; Natural Resources Canada","funders":"Natural Sciences and Engineering Research Council of Canada; University of Victoria; Natural Resources Canada; National Aeronautics and Space Administration","keywords":"Hyperspectral imaging; Remote sensing; Environmental science; Satellite; Vegetation (pathology); Tree canopy; Random forest; Forest health; Computer science; Canopy; Geography; Agroforestry; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002917962,0.0004396319,0.0002732164,0.001880441,0.0003538646,0.0009903192,0.0003249897,0.0003472243,0.004196125],"category_scores_gemma":[0.0006178581,0.0001790486,0.0002729823,0.002086433,0.0002590021,0.0009781711,0.0007667632,0.0004313391,0.001396525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005158232,"about_ca_system_score_gemma":0.000598044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02277028,"about_ca_topic_score_gemma":0.06180687,"domain_scores_codex":[0.9997054,0.00002696928,0.000006963732,0.00003263495,0.0001901182,0.0000379623],"domain_scores_gemma":[0.9997684,0.00003316153,0.00001697172,0.00003427931,0.0001331661,0.00001408933],"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":[0.0002001089,0.00008345179,0.01095273,0.0003980359,0.00007676189,0.0002469586,0.0001394378,0.01300598,0.08591209,0.008153796,0.03978487,0.8410457],"study_design_scores_gemma":[0.00007474877,0.0001755735,0.1608066,0.0005664595,0.0003749931,0.001585022,0.001039079,0.2370058,0.184335,0.06562962,0.3480673,0.0003397671],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2167712,0.01317403,0.5288796,0.002333439,0.0005110071,0.0003828845,0.03033808,0.006759887,0.20085],"genre_scores_gemma":[0.5860595,0.01206536,0.3434213,0.000903794,0.0003561588,0.0001077578,0.03040288,0.0004328316,0.02625032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02277028,"threshold_uncertainty_score":0.04527551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0037204566682167,"score_gpt":0.1698291914114297,"score_spread":0.1661087347432131,"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."}}