{"id":"W4394346346","doi":"10.6084/m9.figshare.13125170","title":"Herring Gull Abundance in parking lots with/without automobile vehicles","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Herring; Abundance (ecology); Fishery; Herring gull; Larus; Biology; Fish <Actinopterygii>","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":[0.00110064,0.0005003599,0.0007414931,0.0006172042,0.00126606,0.0008866548,0.0006154402,0.0006238055,0.003366933],"category_scores_gemma":[0.001875685,0.0003974015,0.0005441679,0.000288667,0.001299212,0.0006382339,0.0008048314,0.0008462504,0.0006876821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001470392,"about_ca_system_score_gemma":0.0005140734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01583064,"about_ca_topic_score_gemma":0.07300187,"domain_scores_codex":[0.9988546,0.0002410135,0.00005344555,0.0004537062,0.0001669316,0.0002302323],"domain_scores_gemma":[0.998042,0.0005682255,0.0003763231,0.0001289435,0.0002509962,0.0006334509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.02866549,0.02385665,0.6085358,0.0007405659,0.0004587993,0.002276937,0.009313397,0.0009243109,0.2871906,0.0005042271,0.004765386,0.03276804],"study_design_scores_gemma":[0.0001941432,0.01841109,0.9671801,0.00003884289,0.0001377158,0.0001998888,0.00536406,0.0008718921,0.005058971,0.0002130963,0.002266099,0.00006421472],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9990453,0.00002694512,0.00005721936,0.00003535768,0.00001220772,0.00002859371,0.00009834475,0.00001145005,0.0006845996],"genre_scores_gemma":[0.9949095,0.00005747801,0.0007031776,0.0003529726,0.00002096522,0.0002164814,0.0005106967,0.00001187251,0.003216847],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01583064,"threshold_uncertainty_score":0.03147697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03374838257597437,"score_gpt":0.2637709994506013,"score_spread":0.2300226168746269,"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."}}