{"id":"W2154639572","doi":"10.1371/journal.pbio.1001634","title":"Spatially Explicit Data: Stewardship and Ethical Challenges in Science","year":2013,"lang":"en","type":"article","venue":"PLoS Biology","topic":"Research Data Management Practices","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Stewardship (theology); Scope (computer science); Data sharing; Relation (database); Data science; Engineering ethics; Data management; Replication (statistics); Data curation; Ethical issues; Biology; Knowledge management; Computer science; Political science; Database; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2816377,0.0007099813,0.001751262,0.003300599,0.01894123,0.02935684,0.006212407,0.01404421,0.003172529],"category_scores_gemma":[0.3215097,0.001450324,0.001157349,0.00615534,0.1072315,0.03751677,0.02783711,0.01662323,0.0009781884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01010687,"about_ca_system_score_gemma":0.03227369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005794077,"about_ca_topic_score_gemma":0.006102977,"domain_scores_codex":[0.6786474,0.2450762,0.01627101,0.01746378,0.03782563,0.004715978],"domain_scores_gemma":[0.4451084,0.3925175,0.02978757,0.09957669,0.02444984,0.008560078],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005148303,0.0000497567,0.003435782,0.00036841,0.00006415457,0.0004165922,0.04592806,0.00115265,0.0005636336,0.8901327,0.01109053,0.04674626],"study_design_scores_gemma":[0.00002730417,0.00003730956,0.0009624144,0.0007714253,0.00002711402,0.0003602094,0.0179473,0.001035307,0.0006445564,0.8681918,0.1099219,0.00007346033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03088226,0.01067952,0.168745,0.7572174,0.001933735,0.0002462643,0.0001906642,0.000189251,0.02991595],"genre_scores_gemma":[0.73882,0.008097207,0.1761549,0.06427648,0.003020792,0.001334175,0.0002250959,0.0003193679,0.007752024],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7183623,"threshold_uncertainty_score":0.8858687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3438626461642547,"score_gpt":0.4044572490824292,"score_spread":0.06059460291817453,"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."}}