{"id":"W7140495925","doi":"10.21966/e0r7-ge27","title":"Nanwakolas LiDAR Surveys - Airborne Coastal Observatory","year":2025,"lang":"","type":"dataset","venue":"Hakai Institute","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Watershed; General partnership; Data acquisition; Field (mathematics); Data collection; Plan (archaeology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004079212,0.0003845728,0.0002043787,0.0009312016,0.001052952,0.000935651,0.0005456312,0.0002595378,0.009806909],"category_scores_gemma":[0.0005044754,0.0003328187,0.0002007592,0.001692065,0.0001183733,0.0006294603,0.001036855,0.0005181602,0.004217212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001430225,"about_ca_system_score_gemma":0.003994472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1417641,"about_ca_topic_score_gemma":0.4129722,"domain_scores_codex":[0.9996129,0.0000268384,0.000020731,0.0001007576,0.0001853806,0.00005336573],"domain_scores_gemma":[0.9995013,0.00002165216,0.000054691,0.00005767846,0.0002998839,0.00006470976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004748621,0.0002700872,0.36243,0.000487113,0.0001105681,0.001026742,0.002960437,0.003251582,0.03781768,0.004666208,0.1684488,0.4180559],"study_design_scores_gemma":[0.00006164591,0.0000924918,0.4719687,0.0001589401,0.00003569452,0.0002357261,0.002642531,0.007226888,0.005470562,0.0007947219,0.5112543,0.00005782003],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.5290842,0.00083462,0.01364418,0.001551881,0.0005106676,0.001160336,0.1322538,0.002923399,0.3180369],"genre_scores_gemma":[0.5837918,0.001253974,0.08021165,0.0006300075,0.0001034,0.001658379,0.1275796,0.0007095305,0.2040616],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1417641,"threshold_uncertainty_score":0.2818779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03586112285170674,"score_gpt":0.2823439391019255,"score_spread":0.2464828162502187,"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."}}