{"id":"W4253859377","doi":"10.1002/asi.20664","title":"Description and search labor for information retrieval","year":2007,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University","keywords":"Labor relations; Computer science; Selection (genetic algorithm); Labor history; Information retrieval; Labour economics; Economics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004491743,0.00008004773,0.0001638472,0.0004040464,0.0004680511,0.0002946313,0.0008338505,0.00005559489,1.481424e-7],"category_scores_gemma":[0.0008350892,0.0000554509,0.00009460829,0.00276555,0.001239508,0.005662857,0.0003165178,0.0001757001,5.704369e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001606646,"about_ca_system_score_gemma":0.0003075229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004650904,"about_ca_topic_score_gemma":7.112824e-7,"domain_scores_codex":[0.9986268,0.000008386701,0.0004806793,0.00008846657,0.0005179511,0.0002777374],"domain_scores_gemma":[0.9968067,0.000167258,0.0006866404,0.0002174706,0.0020521,0.00006985741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007802647,0.0000296603,0.002453926,0.00005787297,0.00002676843,9.387112e-8,0.005044335,0.00006738014,0.003920163,0.05656837,0.001116702,0.9306367],"study_design_scores_gemma":[0.005459743,0.006597941,0.1144641,0.0001010947,0.00009368832,0.001236681,0.04216433,0.475722,0.04737002,0.06917971,0.2366237,0.0009870139],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.402568,0.00002255582,0.5909426,0.005931457,0.0002038871,0.0002686659,0.000004387353,0.00002988883,0.0000286616],"genre_scores_gemma":[0.8135498,0.0000586753,0.1849004,0.001442291,0.00003873332,0.000003515808,5.091608e-7,0.000001808345,0.000004306527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9296497,"threshold_uncertainty_score":0.4567018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160569756714476,"score_gpt":0.2839992581284412,"score_spread":0.2679422824569936,"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."}}