{"id":"W2942962169","doi":"10.1109/jstars.2019.2910558","title":"Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation Properties","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Multispectral image; Remote sensing; Mean squared error; Vegetation (pathology); Artificial intelligence; Computer science; Multispectral pattern recognition; Environmental science; Mathematics; Statistics; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001808648,0.0009833809,0.0005123978,0.001621738,0.0001867973,0.0007928477,0.0004540289,0.0007747865,0.000712332],"category_scores_gemma":[0.002951219,0.0002488351,0.0006677131,0.0009440982,0.0002486102,0.001232979,0.0003178801,0.0003763301,0.0004898614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002321548,"about_ca_system_score_gemma":0.0002501601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002599889,"about_ca_topic_score_gemma":0.003474147,"domain_scores_codex":[0.9991363,0.0002101835,0.0000360451,0.0002098226,0.00034106,0.00006666474],"domain_scores_gemma":[0.9986467,0.0006620437,0.0001413543,0.0001422481,0.0003728223,0.0000348258],"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":[0.001212771,0.0004086492,0.03424248,0.000819559,0.0005275614,0.000208355,0.0002113393,0.1258139,0.2718698,0.000867191,0.001176293,0.5626421],"study_design_scores_gemma":[0.00003372944,0.0004875164,0.06835797,0.00004587996,0.0002724632,0.0002764727,0.0002044467,0.7683508,0.1592645,0.0006051655,0.001982916,0.0001180378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6666596,0.002788817,0.3221786,0.0001884206,0.0001055798,0.00009342113,0.0005182063,0.002033028,0.005434302],"genre_scores_gemma":[0.7772819,0.00132748,0.2187701,0.00009096066,0.00003682978,0.00005541283,0.0008105199,0.0001730904,0.001453757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002599889,"threshold_uncertainty_score":0.009565115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997800694758447,"score_gpt":0.2116476444064835,"score_spread":0.1916696374588991,"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."}}