{"id":"W3202520145","doi":"10.18280/ts.380424","title":"DenseResUNet: An Architecture to Assess Water-Stressed Sugarcane Crops from Sentinel-2 Satellite Imagery","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Segmentation; Computer science; Block (permutation group theory); Agricultural engineering; Environmental science; Vegetation (pathology); Artificial intelligence; Mathematics; Engineering; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002161021,0.0003626033,0.0003006458,0.00004023471,0.0001947999,0.0002652966,0.0003708668,0.0001221295,0.006393319],"category_scores_gemma":[0.0000194261,0.0002602235,0.0001237303,0.000279251,0.0001091308,0.000194383,0.0002730393,0.0002870132,0.0007456618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001420397,"about_ca_system_score_gemma":0.00001819343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004033251,"about_ca_topic_score_gemma":0.00103806,"domain_scores_codex":[0.9970506,0.0002669915,0.0004000698,0.0008638503,0.0007565952,0.0006618656],"domain_scores_gemma":[0.9989277,0.00007596846,0.00007105163,0.0004943336,0.00004125192,0.0003897205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005644118,0.0002159027,0.01126419,0.000009863877,0.00004131186,0.0004815156,0.00150045,0.006449122,0.9655034,0.000004510774,0.00303147,0.01144183],"study_design_scores_gemma":[0.000738986,0.00008867011,0.4027406,0.00006275639,0.00006437965,0.00007963154,0.0003180965,0.0007746217,0.5637616,0.0004773072,0.03019329,0.0007000669],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915408,0.00004138441,0.001923319,0.001833463,0.0001898327,0.0002728899,0.00004430621,0.0001234395,0.004030561],"genre_scores_gemma":[0.9878581,0.00001185255,0.008453708,0.001659465,0.0003768255,0.000004901905,0.0005069796,0.00004235905,0.001085822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4017418,"threshold_uncertainty_score":0.999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685500668471555,"score_gpt":0.2273970600948575,"score_spread":0.2105420534101419,"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."}}