{"id":"W4412734605","doi":"10.1017/eds.2025.10013.pr13","title":"Decision: Tree semantic segmentation from aerial image time series — R2/PR13","year":2025,"lang":"en","type":"peer-review","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Computer Research Institute of Montréal","funders":"","keywords":"Aerial image; Segmentation; Decision tree; Series (stratigraphy); Tree (set theory); Artificial intelligence; Computer science; Image (mathematics); Pattern recognition (psychology); Computer vision; Mathematics; Geology; Combinatorics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002934,0.002208857,0.001173486,0.002259733,0.0005566786,0.001520668,0.002697828,0.001909673,0.01444664],"category_scores_gemma":[0.008473124,0.0005609847,0.001396243,0.001491127,0.0005647477,0.001947129,0.001352164,0.001620704,0.02123434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009465538,"about_ca_system_score_gemma":0.002927475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01496522,"about_ca_topic_score_gemma":0.02620512,"domain_scores_codex":[0.9982882,0.0002686545,0.0001035199,0.0005707832,0.0005835952,0.0001852228],"domain_scores_gemma":[0.9980333,0.0004070754,0.0001708478,0.0004355693,0.0008099383,0.0001431251],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008555791,0.0004077234,0.010025,0.0009194115,0.0006162567,0.0003330192,0.0001191767,0.06419913,0.01834457,0.003693029,0.3734132,0.5270739],"study_design_scores_gemma":[0.000229379,0.0002345791,0.007835101,0.000129689,0.0001125067,0.0002988056,0.000162003,0.8485107,0.02678773,0.006902906,0.1087048,0.00009174998],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1007309,0.004198618,0.5582213,0.002234374,0.001521556,0.0016611,0.06962905,0.2438918,0.01791128],"genre_scores_gemma":[0.2863343,0.001209657,0.4670177,0.0008131989,0.0004138681,0.001143029,0.2132723,0.007768773,0.0220271],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.997066,"threshold_uncertainty_score":0.04832882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009536046522162448,"score_gpt":0.2689718646567028,"score_spread":0.2594358181345403,"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."}}