{"id":"W2025333233","doi":"10.1002/gea.20281","title":"Detection of a low‐relief 18th‐century British siege trench using LiDAR vegetation penetration capabilities at Fort Beauséjour–Fort Cumberland National Historic Site, Canada","year":2009,"lang":"en","type":"article","venue":"Geoarchaeology","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Parks Canada","funders":"","keywords":"Lidar; Remote sensing; Aerial photography; Digital elevation model; Siege; Vegetation (pathology); Geology; Historic site; Archaeology; Digital surface; National park; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004470276,0.0001558414,0.0002432958,0.0001731589,0.0003660554,0.00001115769,0.0001433042,0.0001683943,0.0006921025],"category_scores_gemma":[0.0003152463,0.0001625261,0.00006236442,0.0003042527,0.0003941506,0.0002434751,0.00003012633,0.0002535691,0.00001082863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002383575,"about_ca_system_score_gemma":0.0005406011,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6501069,"about_ca_topic_score_gemma":0.9676801,"domain_scores_codex":[0.9978766,0.0001869477,0.0004086334,0.0003628061,0.0006718335,0.0004932041],"domain_scores_gemma":[0.9990532,0.0002290334,0.0001766973,0.000122192,0.0002465167,0.0001723621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001575943,0.0001249069,0.5556428,0.000353224,0.0001072199,0.000223216,0.003732511,0.03792337,0.01449862,0.0004227466,0.000609364,0.3847861],"study_design_scores_gemma":[0.0006250367,0.001411279,0.961633,0.00003278118,0.00001632642,0.0003694414,0.0001128073,0.01475735,0.001118941,0.01504511,0.004580818,0.000297077],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947351,0.001554892,0.001519933,0.0002014507,0.0003810518,0.0003468627,0.00007705297,0.00002996113,0.001153737],"genre_scores_gemma":[0.998229,0.000218357,0.0006533445,0.0001370003,0.0001856847,0.000005898514,0.0003074167,0.000003789718,0.0002594687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4059902,"threshold_uncertainty_score":0.7578037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01092609242531451,"score_gpt":0.2173979223031336,"score_spread":0.2064718298778191,"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."}}