{"id":"W2141434504","doi":"10.1109/ccst.1988.75984","title":"DMSA Line (intrusion detection line sensor)","year":2003,"lang":"en","type":"article","venue":"","topic":"Near-Field Optical Microscopy","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Senstar (Canada)","funders":"","keywords":"Line (geometry); Intrusion detection system; Intrusion; Terrain; Computer science; Artificial intelligence; Real-time computing; Geology; Geography; Mathematics","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":[0.0002363518,0.0005521086,0.0004773344,0.000893604,0.0004209402,0.0006850561,0.0008899519,0.0007048485,0.02018268],"category_scores_gemma":[0.0003716438,0.0002559327,0.0002085915,0.0007247757,0.0001737869,0.0008925293,0.0003814001,0.0005096003,0.01138274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006218428,"about_ca_system_score_gemma":0.0003592027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009503029,"about_ca_topic_score_gemma":0.002037998,"domain_scores_codex":[0.9995864,0.00004136125,0.00001603974,0.00006869093,0.0002613314,0.00002610345],"domain_scores_gemma":[0.999835,0.00002603776,0.00002127626,0.00002802805,0.00006665005,0.00002294275],"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":[0.0004477687,0.0002019304,0.004135274,0.00104736,0.00005980404,0.0003904151,0.0001303884,0.004047061,0.3987427,0.02140751,0.296162,0.2732278],"study_design_scores_gemma":[0.00004147796,0.0001401919,0.002758516,0.00004294285,0.00002276071,0.00113212,0.00004258218,0.0180746,0.1579227,0.001277783,0.8184904,0.00005397687],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04706596,0.008551133,0.6111675,0.003364519,0.001810808,0.001385343,0.0497263,0.06185457,0.2150739],"genre_scores_gemma":[0.2169328,0.004879856,0.5144275,0.002603526,0.0003839995,0.001055934,0.03105868,0.001540387,0.2271172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02018268,"threshold_uncertainty_score":0.06751776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007858674647207714,"score_gpt":0.2167741961944034,"score_spread":0.2089155215471957,"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."}}