{"id":"W6964601504","doi":"10.25773/xkcn-sh60","title":"GIS Data: Prince George’s County, Maryland – Shoreline Inventory Data 2023","year":2023,"lang":"en","type":"dataset","venue":"W&M Publish (College of William & Mary)","topic":"Ecology and biodiversity studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Shore; Shapefile; Marsh; Geographic information system; Aerial imagery; Hydrology (agriculture); Riparian zone; Coastal erosion; Recreation","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":[0.0003655135,0.0005122322,0.0002824253,0.004081947,0.0006054588,0.0008370568,0.0008496402,0.0002509859,0.05392146],"category_scores_gemma":[0.002372572,0.000383957,0.0002656869,0.009694948,0.0001363264,0.0005824263,0.0006794753,0.0005563545,0.02728911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232144,"about_ca_system_score_gemma":0.002584977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1424914,"about_ca_topic_score_gemma":0.1424391,"domain_scores_codex":[0.9995871,0.00004145961,0.00004850754,0.00009571201,0.00016421,0.0000629904],"domain_scores_gemma":[0.9981714,0.0001724093,0.0001854203,0.0001857878,0.001173083,0.0001117643],"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":[0.0000417047,0.0000264228,0.007673142,0.0001631185,0.00001328105,0.00007931192,0.0001701903,0.0007674481,0.0001984184,0.0007083503,0.9769109,0.01324778],"study_design_scores_gemma":[0.00003646197,0.00001517512,0.05598405,0.0001066102,0.00001521733,0.0001099678,0.0008480934,0.001561987,0.0007851923,0.0005529,0.939953,0.00003115247],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00587229,0.00004372384,0.0007240899,0.0001687358,0.0000313316,0.0001003479,0.9774274,0.0007865583,0.01484563],"genre_scores_gemma":[0.01448053,0.00009534643,0.004145292,0.00008430413,0.00001253067,0.0006557734,0.9699,0.0002048177,0.01042144],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1424914,"threshold_uncertainty_score":0.283324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04526652772705658,"score_gpt":0.2593016392680508,"score_spread":0.2140351115409942,"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."}}