{"id":"W4396699773","doi":"10.1002/ecm.1605","title":"Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis","year":2024,"lang":"en","type":"article","venue":"Ecological Monographs","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University; Université de Montréal; Université de Sherbrooke","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Royal Canadian Geographical Society","keywords":"Hyperspectral imaging; Biodiversity; Variation (astronomy); Canopy; Ecology; Geography; Tree (set theory); Aerial survey; Environmental science; Remote sensing; Agroforestry; Biology; Mathematics","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":[0.007735633,0.0004411007,0.0002807795,0.002094516,0.0007735872,0.002224636,0.0008952407,0.000582835,0.001930251],"category_scores_gemma":[0.01897722,0.0001831108,0.0007632861,0.002335236,0.001295228,0.00109603,0.001125382,0.0006268537,0.0001716555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001413956,"about_ca_system_score_gemma":0.001160725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09661754,"about_ca_topic_score_gemma":0.0551011,"domain_scores_codex":[0.9977183,0.0009983116,0.00008804857,0.0004884569,0.0005283076,0.0001786451],"domain_scores_gemma":[0.9754926,0.01649076,0.002852549,0.001075175,0.003558372,0.0005305853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001462479,0.00005472965,0.978459,0.00004907535,0.0005060061,0.0001605938,0.0004233576,0.003701946,0.00367482,0.001351067,0.000264387,0.01120866],"study_design_scores_gemma":[0.000009095589,0.00009250854,0.9606259,0.00003634335,0.0001510183,0.0001173194,0.001961007,0.03341825,0.0009939926,0.002006755,0.0005632245,0.00002469249],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990355,0.0002589527,0.005911443,0.0002901181,0.000009593699,0.00001505035,0.0002113543,0.00003643075,0.002912],"genre_scores_gemma":[0.9989501,0.00002516676,0.0007365499,0.00004128721,0.000006361558,0.000004041721,0.0000975597,0.000004521818,0.0001345613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09661754,"threshold_uncertainty_score":0.1921104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03655491148488158,"score_gpt":0.2275955354670601,"score_spread":0.1910406239821785,"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."}}