{"id":"W2123786420","doi":"10.1111/0031-868x.t01-1-00010","title":"Autonomous space resection using Point‐ and Line‐Based representation of FREE‐FORM control Linear Features","year":2003,"lang":"en","type":"article","venue":"The Photogrammetric Record","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"3v Geomatics (Canada); University of Calgary","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Representation (politics); Feature (linguistics); Photogrammetry; Object (grammar); Feature vector; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004618758,0.0001129164,0.000160207,0.0001283755,0.0001931831,0.00003090179,0.0001411865,0.00006673177,0.00007988508],"category_scores_gemma":[0.0004373308,0.00008421281,0.00007088289,0.001395585,0.0001630852,0.00006877873,0.00003288732,0.0001547948,0.00001094868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008451375,"about_ca_system_score_gemma":0.00001576766,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01389359,"about_ca_topic_score_gemma":0.0006368728,"domain_scores_codex":[0.9989856,0.0001351947,0.0002239998,0.0002450892,0.0002183563,0.0001918004],"domain_scores_gemma":[0.9989413,0.0002857611,0.000186513,0.000496301,0.00002382203,0.00006627335],"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":[0.0008244985,0.0008006528,0.1152896,0.00006987386,0.0002112501,0.00001120662,0.002163623,0.04030523,0.2794991,0.0007842645,0.006586587,0.5534541],"study_design_scores_gemma":[0.005307728,0.0009642978,0.1071419,0.00006171547,0.0003759677,0.000215485,0.001032007,0.4821056,0.3660462,0.00970085,0.02618194,0.0008662387],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279286,0.00007462153,0.06245044,0.0003563272,0.000154142,0.0006241446,0.000006778764,0.0000495064,0.00835548],"genre_scores_gemma":[0.9840706,0.00002664573,0.01548348,0.00008999,0.00003380364,0.000004809307,0.000003064129,0.00001455025,0.0002730635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5525879,"threshold_uncertainty_score":0.992673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02145637385036384,"score_gpt":0.2671244061497107,"score_spread":0.2456680322993469,"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."}}