{"id":"W4308509959","doi":"10.3390/rs14225633","title":"A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Agriculture and Agri-Food Canada; Centre For Cold Ocean Resources Engineering; Institut National de la Recherche Scientifique","funders":"","keywords":"Food security; Environmental science; Normalized Difference Vegetation Index; Remote sensing; Agricultural engineering; Crop; Climate change; Agriculture; Geography; Ecology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001073393,0.0002565944,0.0009283795,0.000254129,0.0004418145,0.00004842839,0.0001607276,0.0001211203,0.00003605619],"category_scores_gemma":[0.000421175,0.0002083985,0.0005140768,0.001518929,0.0002614318,0.0001120976,0.0005472002,0.0002363433,0.000001761733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001604609,"about_ca_system_score_gemma":0.00000887939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003972174,"about_ca_topic_score_gemma":0.0000844073,"domain_scores_codex":[0.9978341,0.0003524804,0.0004518328,0.0006065961,0.0004158845,0.0003390322],"domain_scores_gemma":[0.9985723,0.0003947827,0.0004550522,0.0004961482,0.00004484642,0.00003682532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002629178,0.000003779455,0.000004442042,0.00001353406,0.005906601,0.00001001899,0.0005522238,0.008193512,0.491664,0.000005218083,0.00004315216,0.4935773],"study_design_scores_gemma":[0.0001911538,0.00009145673,0.0008149477,0.000006832941,0.04442903,0.0001487292,0.001570832,0.8995693,0.04612774,0.002291748,0.004312993,0.0004452883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5177181,0.0002183241,0.4790376,0.001664665,0.0001384356,0.0005229691,0.00003821707,0.0002965508,0.0003651947],"genre_scores_gemma":[0.2840363,0.00004170366,0.7153054,0.00017616,0.00001521275,1.411486e-8,0.00005476189,0.00002720487,0.0003431962],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8913757,"threshold_uncertainty_score":0.8498246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08857159323546317,"score_gpt":0.2954807042263082,"score_spread":0.206909110990845,"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."}}