{"id":"W2737221650","doi":"10.1111/1540-6229.12209","title":"Big‐Box Stores and Urban Land Prices: Friend or Foe?","year":2017,"lang":"en","type":"article","venue":"Real Estate Economics","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Locale (computer software); Agricultural economics; Quarter (Canadian coin); Mile; Economics; Land price; Advertising; Business; Geography; Computer science","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":[0.001176537,0.0002097701,0.000354609,0.0004804161,0.0003366633,0.001297858,0.0004647895,0.0009251958,0.01219919],"category_scores_gemma":[0.007528332,0.0001579418,0.0003762905,0.0007737586,0.0009351154,0.00227254,0.0006698693,0.0008074744,0.0007176484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003090391,"about_ca_system_score_gemma":0.0002471191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01118313,"about_ca_topic_score_gemma":0.01884443,"domain_scores_codex":[0.9995567,0.0002165496,0.00001705257,0.00008842541,0.00007166142,0.0000496609],"domain_scores_gemma":[0.9897256,0.005427201,0.002229863,0.0006222346,0.0009858561,0.001009122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003558929,0.0002074462,0.9408208,0.0001313172,0.0005187711,0.0004878209,0.00201805,0.0004311881,0.0003237527,0.00442577,0.01413672,0.03614264],"study_design_scores_gemma":[0.0000329147,0.0002744813,0.9646512,0.0002394176,0.0003451021,0.0005212587,0.01064431,0.002051429,0.0005422823,0.006580294,0.01406233,0.00005492287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.961722,0.004322594,0.0005775131,0.02482486,0.0003190036,0.00000688026,0.0004715309,0.00002926729,0.007726326],"genre_scores_gemma":[0.9972751,0.0005681055,0.0001593282,0.0008311167,0.0002273839,0.000001489711,0.00007952705,0.000008979121,0.0008489878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01219919,"threshold_uncertainty_score":0.04081035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04353213204744,"score_gpt":0.2308312308957584,"score_spread":0.1872990988483184,"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."}}