{"id":"W2184578674","doi":"10.82308/10104","title":"Application of machine learning methods and airborne hyperspectral remote sensing for crop yield estimation","year":2003,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Remote sensing; Yield (engineering); Crop; Computer science; Environmental science; Artificial intelligence; Machine learning; Geology; Geography; Forestry; Materials science","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":[0.0006656487,0.0003919413,0.0001549341,0.0004798609,0.00009312187,0.0003288692,0.000164016,0.0002041983,0.0006568491],"category_scores_gemma":[0.00180904,0.00009621951,0.0001851625,0.0005191353,0.0001278134,0.0004042857,0.0001835604,0.000232705,0.0001277344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003133086,"about_ca_system_score_gemma":0.000285005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003565506,"about_ca_topic_score_gemma":0.003368448,"domain_scores_codex":[0.9997758,0.00007590574,0.00001235856,0.00003360045,0.00008864095,0.00001356285],"domain_scores_gemma":[0.9995245,0.0003238477,0.0000409566,0.00002516364,0.00007932779,0.000006216047],"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":[0.0001197039,0.0001928239,0.01394432,0.0001371887,0.00009421617,0.00007823588,0.00008703107,0.2770512,0.01730067,0.003549261,0.0008153381,0.6866301],"study_design_scores_gemma":[0.000009870087,0.00009591927,0.01144393,0.00001779458,0.00002121249,0.00004092302,0.00004869675,0.9731039,0.009290656,0.002945464,0.002967868,0.00001371861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3565125,0.002347659,0.6285945,0.0004420308,0.00008928215,0.00009618947,0.0001534776,0.000364557,0.01139986],"genre_scores_gemma":[0.7950924,0.001911478,0.1992455,0.00006495936,0.00007748141,0.00005702033,0.0001965787,0.00002376093,0.003330864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003565506,"threshold_uncertainty_score":0.007089555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299432761797088,"score_gpt":0.2699187405528816,"score_spread":0.2569244129349107,"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."}}