{"id":"W2045528474","doi":"10.5194/isprsarchives-xl-2-243-2014","title":"Examining Map Projection Distortions using Geospatial Web Tools","year":2014,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Historical Geography and Cartography","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Mitacs","keywords":"Geospatial analysis; Web mapping; Web Coverage Service; Computer science; Projection (relational algebra); Geospatial PDF; Set (abstract data type); Map projection; Web service; Web application; World Wide Web; Web modeling; Information retrieval; Cartography; Geography; Artificial intelligence; Programming language; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002528338,0.0005361246,0.0003129182,0.0045162,0.0005665629,0.002744624,0.0005343904,0.0003430613,0.005455869],"category_scores_gemma":[0.03479658,0.0003582475,0.0002567243,0.00610381,0.0006495026,0.002520827,0.001777383,0.0005888259,0.0006577565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007483367,"about_ca_system_score_gemma":0.00073091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009499616,"about_ca_topic_score_gemma":0.01002619,"domain_scores_codex":[0.9969077,0.000751837,0.0001848584,0.0002559514,0.001783865,0.0001157114],"domain_scores_gemma":[0.977745,0.01248212,0.002085797,0.00189116,0.005603793,0.0001922242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001436841,0.0002499597,0.2692291,0.001080313,0.000328294,0.001644291,0.009909106,0.06271426,0.02139109,0.01535419,0.007673327,0.6089892],"study_design_scores_gemma":[0.0001552534,0.0004277944,0.4627147,0.0004402427,0.0003528464,0.003997497,0.01941607,0.3762425,0.06900322,0.01567397,0.05126602,0.0003097787],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8874145,0.0004178962,0.08877805,0.0003223865,0.00008048774,0.0002636139,0.002473258,0.002400404,0.01784951],"genre_scores_gemma":[0.9633574,0.000172139,0.03418541,0.00001622821,0.00001003484,0.00005167793,0.001079669,0.0003766552,0.0007508267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009499616,"threshold_uncertainty_score":0.01888865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03135314136156736,"score_gpt":0.2728845346769632,"score_spread":0.2415313933153959,"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."}}