{"id":"W4393477507","doi":"10.5281/zenodo.10265706","title":"iPhone Lidar - Stanley Park","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Geography; Archaeology; Remote sensing","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":[0.0005154951,0.002515778,0.001513557,0.00185968,0.001024304,0.001398313,0.002405968,0.001130492,0.03285922],"category_scores_gemma":[0.001249032,0.0005773616,0.0006206275,0.004730082,0.0003160826,0.0009669859,0.001269942,0.001359908,0.08885337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001920526,"about_ca_system_score_gemma":0.002852505,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2797058,"about_ca_topic_score_gemma":0.4634916,"domain_scores_codex":[0.9993101,0.00005154712,0.00003268289,0.0002057883,0.000250726,0.0001491234],"domain_scores_gemma":[0.9991947,0.00005137299,0.00003234549,0.0001916995,0.0004195468,0.0001102976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000319128,0.00001380837,0.0004891278,0.0001209022,0.00001157738,0.00001477832,0.00001241285,0.0002016478,0.000139649,0.0001131459,0.9964334,0.002417666],"study_design_scores_gemma":[0.0001590295,0.0000181697,0.009329392,0.0001619281,0.00002382707,0.00007116346,0.0001526091,0.001279906,0.001365751,0.0007812718,0.9866126,0.00004425771],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004594544,0.00005579321,0.0001483325,0.00003004424,0.00002134828,0.00001528651,0.9959591,0.001216156,0.002094496],"genre_scores_gemma":[0.0007442408,0.00003152754,0.0003017221,0.00001214958,0.000003145595,0.00002250064,0.9979314,0.0001208713,0.0008325203],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7202942,"threshold_uncertainty_score":0.5561554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0224903023244068,"score_gpt":0.2376329049411843,"score_spread":0.2151426026167775,"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."}}