{"id":"W2040014370","doi":"10.1080/01431161.2013.828183","title":"Estimation of foliar pigment concentration in floating macrophytes using hyperspectral vegetation indices","year":2013,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Spectroradiometer; Macrophyte; Vegetation (pathology); Pigment; Environmental science; Photosynthetic pigment; Chlorophyll; Wetland; Aquatic plant; Chlorophyll a; Botany; Reflectivity; Ecology; Chemistry; Remote sensing; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002676361,0.0001123295,0.0001697268,0.0001049089,0.00003603441,0.00006947115,0.0001469753,0.00006185956,0.0000320138],"category_scores_gemma":[0.0001431016,0.00009686263,0.0000729525,0.0001809786,0.00007408312,0.0006309006,0.00003910485,0.0001734941,0.00001272111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005019701,"about_ca_system_score_gemma":0.00002504687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001131294,"about_ca_topic_score_gemma":0.00006921345,"domain_scores_codex":[0.9983749,0.00007539459,0.0006054078,0.0001270377,0.000669663,0.0001476466],"domain_scores_gemma":[0.9988944,0.00006617749,0.0007771107,0.00006567948,0.0001496352,0.00004703156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001973973,0.00002335368,0.00165725,0.000007318949,0.0000299519,0.00002917146,0.001586985,0.2134443,0.4222938,0.00001240134,0.00002127979,0.3608744],"study_design_scores_gemma":[0.0004999812,0.00004824241,0.03295334,0.0004146729,0.00001741304,0.0003189457,0.0004041052,0.8590697,0.1041705,0.001969145,0.00001587473,0.0001180299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9587323,0.00004923255,0.03955442,0.0003021639,0.0005444532,0.0001176786,3.514436e-7,0.000006228941,0.0006931456],"genre_scores_gemma":[0.7700926,0.00001320688,0.2297156,0.00004692551,0.0001159941,4.651337e-9,0.000001326245,0.000007032127,0.000007296939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6456254,"threshold_uncertainty_score":0.3949943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008587533752918377,"score_gpt":0.2470837340869198,"score_spread":0.2384962003340014,"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."}}