{"id":"W4353100317","doi":"10.18280/ts.400115","title":"Calibration Method of Feature Point Layout in Prefabricated Buildings Based on Image Recognition Technology","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Point (geometry); Feature extraction; Calibration; Artificial intelligence; Computer science; Image (mathematics); Computer vision; Pattern recognition (psychology); Feature recognition; Feature detection (computer vision); Image processing; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004322015,0.00009427427,0.0001387742,0.0003940398,0.0000413295,0.00001991144,0.00007515966,0.00009103195,0.0004597558],"category_scores_gemma":[0.00003633948,0.00007477432,0.00003619208,0.0007150773,0.00002847318,0.00008599001,0.000003526735,0.0001307476,0.00004167396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005194599,"about_ca_system_score_gemma":0.00002279728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005098433,"about_ca_topic_score_gemma":0.0002224633,"domain_scores_codex":[0.9991601,0.000102785,0.0001829347,0.0001988692,0.0001765561,0.0001787709],"domain_scores_gemma":[0.9996576,0.000115266,0.000074804,0.00008740772,0.00002927722,0.00003562447],"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":[0.0007646883,0.0001785842,0.09669365,0.0001259407,0.00004078814,0.0001101588,0.0007886873,0.06195414,0.04249298,0.00004350167,0.005462636,0.7913442],"study_design_scores_gemma":[0.0009215235,0.0004210697,0.1534944,0.0001047083,0.00001607809,0.000005740851,0.0001400902,0.8245685,0.01869942,0.0009005103,0.0005639598,0.0001639976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854823,0.0000135764,0.009494744,0.002700856,0.00008139374,0.0002700849,0.00009281659,0.0001684978,0.001695723],"genre_scores_gemma":[0.9886175,0.000006098065,0.01067594,0.0001721622,0.00003130098,6.453228e-7,0.0004525745,0.000003871674,0.0000398535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7911803,"threshold_uncertainty_score":0.5034003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01808710218871204,"score_gpt":0.2413169636434731,"score_spread":0.2232298614547611,"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."}}