{"id":"W4404355422","doi":"10.18280/ts.410517","title":"ContexNestedU-Net: Efficient Context-Aware Semantic Segmentation Architecture for Precision Agriculture Applications Based on Multispectral Remote Sensing Imagery","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multispectral image; Computer science; Segmentation; Context (archaeology); Artificial intelligence; Precision agriculture; Remote sensing; Architecture; Computer vision; Agriculture; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001824755,0.0006524178,0.0003913567,0.0004261954,0.0002493328,0.0004714549,0.0008916028,0.0005157028,0.001593502],"category_scores_gemma":[0.0002896111,0.000272302,0.0004620836,0.0003763129,0.0002395292,0.0009661369,0.0005315616,0.0004177767,0.0004885847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006444425,"about_ca_system_score_gemma":0.0006849758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01020356,"about_ca_topic_score_gemma":0.01923873,"domain_scores_codex":[0.9999094,0.000008520775,0.00000446073,0.00003658421,0.00002168696,0.00001936797],"domain_scores_gemma":[0.9999371,0.0000119509,0.000009204934,0.00001128239,0.00002200696,0.000008532053],"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":[0.0005740212,0.0002237053,0.003811023,0.0001523743,0.0001652127,0.0002293616,0.0001120381,0.3308546,0.05349314,0.006120429,0.007670305,0.5965937],"study_design_scores_gemma":[0.000007202249,0.00007376714,0.0007945481,0.000009323897,0.00002765476,0.00005156106,0.00001635841,0.9842944,0.01071375,0.001571805,0.002428265,0.00001130787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1492549,0.001343079,0.8326482,0.0002254535,0.0001837422,0.00009504715,0.0005859939,0.009553488,0.006109875],"genre_scores_gemma":[0.7399226,0.0005428021,0.2503078,0.0002841621,0.00004829852,0.00008636721,0.001629367,0.000199406,0.006979086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01020356,"threshold_uncertainty_score":0.02028835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362606861272439,"score_gpt":0.2348583928869729,"score_spread":0.2212323242742485,"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."}}