{"id":"W4362717754","doi":"10.1038/s41597-023-02096-0","title":"A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research","year":2023,"lang":"en","type":"article","venue":"Scientific Data","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":182,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"European Space Agency; Deutsche Forschungsgemeinschaft; Niedersächsische Ministerium für Wissenschaft und Kultur","keywords":"Remote sensing; Information retrieval; Earth (classical element); Computer science; Geography; Astronomy; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003523264,0.00158187,0.00102116,0.01124117,0.0008655476,0.002906895,0.002010838,0.0009564392,0.02853676],"category_scores_gemma":[0.01196868,0.0007008494,0.001317603,0.01251644,0.0006501494,0.003744252,0.003176822,0.002248255,0.03093117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009671606,"about_ca_system_score_gemma":0.004344397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004965517,"about_ca_topic_score_gemma":0.005864324,"domain_scores_codex":[0.997236,0.0004009894,0.0006014709,0.0003136405,0.001287422,0.0001605115],"domain_scores_gemma":[0.990858,0.001502445,0.0008109485,0.002320768,0.003966451,0.000541462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001863217,0.0001901579,0.004695056,0.002366962,0.00009233549,0.0001768278,0.0003737732,0.003713199,0.01072874,0.02930515,0.6415552,0.3066163],"study_design_scores_gemma":[0.00003303212,0.00003926345,0.007014628,0.0004630527,0.00003860642,0.0003152213,0.00009243191,0.003197528,0.003846047,0.01384251,0.9710109,0.0001066232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.01107487,0.002921483,0.4905255,0.0009829164,0.001386371,0.001477791,0.3411281,0.06049784,0.09000504],"genre_scores_gemma":[0.01852729,0.003173442,0.4486486,0.0007627443,0.0004735366,0.001872577,0.4885919,0.01829328,0.01965664],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.02853676,"threshold_uncertainty_score":0.095465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1245422815350868,"score_gpt":0.3377334745517816,"score_spread":0.2131911930166948,"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."}}