{"id":"W4387217590","doi":"10.1002/lom3.10576","title":"A new index for the rapid generation of chlorophyll time series from hyperspectral imaging of sediment cores","year":2023,"lang":"en","type":"article","venue":"Limnology and Oceanography Methods","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal; Université Laval; Université du Québec à Montréal","funders":"Groupe de recherche interuniversitaire en limnologie; Fonds Québécois de la Recherche sur la Nature et les Technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation","keywords":"Hyperspectral imaging; Sediment; Environmental science; Remote sensing; Chlorophyll; Chlorophyll a; Geology; Biology; Botany","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004491333,0.00008735053,0.0001990061,0.0001281135,0.00009466844,0.00001215192,0.0001108168,0.00005222129,0.0002858452],"category_scores_gemma":[0.00002988583,0.00005896874,0.00008610768,0.0002813794,0.0001192886,0.00008336346,0.00001843441,0.00005915813,0.00000195471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":4.426694e-7,"about_ca_system_score_gemma":0.00002343106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000979063,"about_ca_topic_score_gemma":0.0002357915,"domain_scores_codex":[0.9992897,0.0001362216,0.0001986077,0.0001563613,0.00006775683,0.0001513587],"domain_scores_gemma":[0.9992296,0.0004781326,0.00009968666,0.0001274281,0.00002876343,0.00003644427],"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":[0.0002971985,0.000009894913,0.4902647,0.00004239548,0.0002150237,0.000001810148,0.000924951,0.0002679926,0.005046796,0.0004796118,0.00146121,0.5009884],"study_design_scores_gemma":[0.000922651,0.0007411195,0.8441405,0.00002113078,0.0001574106,0.00001280384,0.001535074,0.106526,0.02763677,0.01053427,0.007522649,0.0002496552],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9696878,0.006173063,0.02112654,0.0008575576,0.0006837495,0.0003452654,0.00017587,0.00005310922,0.0008970137],"genre_scores_gemma":[0.9561206,0.000659704,0.04255192,0.00009100025,0.0002425501,0.000003011889,0.0001285817,0.000004373635,0.0001983026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5007387,"threshold_uncertainty_score":0.3129804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378399139539865,"score_gpt":0.2627036940789852,"score_spread":0.2389197026835866,"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."}}