{"id":"W3007726079","doi":"10.5281/zenodo.3539254","title":"International Research Infrastructure Landscape 2019","year":2019,"lang":"en","type":"article","venue":"Työväentutkimus Vuosikirja","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"College of Pharmacy, University of Michigan; College of Veterinary Medicine, Cornell University; National Institutes of Health; Research Institute of Economy, Trade and Industry; Fudan University; Pennsylvania State University; University of Tokyo; European Commission; Partnership for Advanced Computing in Europe AISBL; University of Chicago; University of Pennsylvania; Australian National University; University of Michigan; York University; National Science Foundation","keywords":"Perspective (graphical); Regional science; Environmental resource management; Geography; Environmental planning; Computer science; Environmental science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003379104,0.0009943472,0.0004840274,0.006140562,0.0008588904,0.01056425,0.0014193,0.002242736,0.06349559],"category_scores_gemma":[0.006479263,0.0005808555,0.0007723067,0.0106608,0.0003648541,0.005344336,0.00352045,0.00218525,0.04738756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004353513,"about_ca_system_score_gemma":0.01021103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01681369,"about_ca_topic_score_gemma":0.01861891,"domain_scores_codex":[0.9965351,0.0002908228,0.0002359573,0.0002521588,0.002088361,0.000597684],"domain_scores_gemma":[0.9960873,0.0002520964,0.0004829253,0.0003734306,0.00208542,0.0007188597],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00006650348,0.00002718214,0.0005874456,0.0002877067,0.00001620376,0.00004898728,0.00005375205,0.0002870126,0.0004288727,0.01497765,0.9476486,0.03557007],"study_design_scores_gemma":[0.000005344718,0.00001291974,0.002173715,0.0001079845,0.000006166217,0.00003249853,0.00004934143,0.00007639177,0.0002160763,0.001004203,0.9963073,0.00000824357],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.004622435,0.0115306,0.005006512,0.01835118,0.005571602,0.0005546417,0.2515662,0.005267772,0.6975291],"genre_scores_gemma":[0.04547788,0.01514402,0.01267099,0.00513364,0.00135677,0.001401842,0.438908,0.003134849,0.476772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9966209,"threshold_uncertainty_score":0.2124139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03979451436865162,"score_gpt":0.2945315968537464,"score_spread":0.2547370824850948,"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."}}