{"id":"W7155953401","doi":"10.1145/3789410.3789413","title":"Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation","year":2025,"lang":"","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Segmentation; Encoder; Convolution (computer science); Benchmark (surveying); Image segmentation; Baseline (sea); Separable space; Component (thermodynamics)","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":[0.0003175097,0.0008968282,0.0005036492,0.0005643804,0.0002223345,0.0008614131,0.0009456983,0.0006756996,0.002896489],"category_scores_gemma":[0.0007228529,0.0002652624,0.0007280251,0.0005682817,0.0004330583,0.001239611,0.0008447112,0.0009116304,0.001451352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006914858,"about_ca_system_score_gemma":0.0009596426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005690419,"about_ca_topic_score_gemma":0.009066366,"domain_scores_codex":[0.9998381,0.00001623949,0.000007877267,0.0000603882,0.00004378996,0.00003357525],"domain_scores_gemma":[0.9998741,0.00003523645,0.00001317037,0.00002861238,0.00003708687,0.00001188691],"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":[0.0004153243,0.0001762344,0.001953468,0.0002262826,0.0001627335,0.0002180149,0.0001284914,0.3296211,0.1125298,0.0157804,0.01147147,0.5273167],"study_design_scores_gemma":[0.000006394215,0.00003612937,0.0003872493,0.000009775441,0.00002469233,0.00007439129,0.00001462626,0.9727341,0.01833627,0.004524826,0.003842708,0.000008823033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04993223,0.0009348436,0.9360906,0.0003811526,0.0001300726,0.00004901174,0.0005233408,0.004656862,0.007301917],"genre_scores_gemma":[0.6416353,0.001028819,0.3401699,0.0005500518,0.00008199473,0.00007984311,0.00220865,0.0006749043,0.01357048],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005690419,"threshold_uncertainty_score":0.01131463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123047784831551,"score_gpt":0.236603721090613,"score_spread":0.2253732432422975,"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."}}