{"id":"W2990593724","doi":"10.1111/ele.13429","title":"Partitioning plant spectral diversity into alpha and beta components","year":2019,"lang":"en","type":"article","venue":"Ecology Letters","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Alpha diversity; Gamma diversity; Beta diversity; Biodiversity; Ecosystem diversity; Optimal distinctiveness theory; Ecology; Diversity (politics); Species diversity; Remote sensing; Environmental science; Geography; Biology","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":[0.0008649048,0.000511303,0.0003009685,0.003227271,0.0003624083,0.0009464803,0.0002560467,0.000292434,0.001064464],"category_scores_gemma":[0.00211363,0.0001950408,0.0004797694,0.001544152,0.0005642215,0.0008086024,0.0007078632,0.0003478727,0.000263004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003708684,"about_ca_system_score_gemma":0.000200256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002007246,"about_ca_topic_score_gemma":0.00236522,"domain_scores_codex":[0.9996662,0.0001010422,0.00001805984,0.00009200664,0.00007955702,0.00004303608],"domain_scores_gemma":[0.9990281,0.0005385107,0.0001317501,0.00009484168,0.0001533812,0.00005345596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007209016,0.000164958,0.2199627,0.0003293856,0.0004429828,0.0002596375,0.002139188,0.06204768,0.1473567,0.01141612,0.001086191,0.5540735],"study_design_scores_gemma":[0.00005059357,0.0002954955,0.5194924,0.0001125757,0.0002762765,0.001166791,0.001703269,0.3777378,0.0413871,0.05078211,0.006813552,0.000181923],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.667032,0.0004960003,0.3261501,0.0001252322,0.0000143817,0.00005918035,0.0003847989,0.0003015305,0.005436775],"genre_scores_gemma":[0.9434099,0.0002315247,0.05510132,0.00003394287,0.00001845514,0.00004426646,0.0003243468,0.00005526089,0.0007811015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003227271,"threshold_uncertainty_score":0.00457412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02548998578581116,"score_gpt":0.1728432502660715,"score_spread":0.1473532644802603,"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."}}