{"id":"W2143108759","doi":"10.1109/ccece.1993.332255","title":"Applying digital image technology to pulp and paper","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Newsprint Company (Canada)","funders":"","keywords":"Reel; Paper machine; Trimming; Mill; Computer science; Calipers; Engineering drawing; Impression; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006800266,0.0006240482,0.0002972769,0.002591857,0.0006433566,0.002956341,0.001091621,0.001219567,0.01641039],"category_scores_gemma":[0.001927093,0.0003171799,0.000375242,0.002837473,0.001276022,0.002143663,0.001382148,0.0008849242,0.006697269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008943289,"about_ca_system_score_gemma":0.0007100482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002539013,"about_ca_topic_score_gemma":0.004289672,"domain_scores_codex":[0.9991454,0.0001092418,0.00005570901,0.000100592,0.0005367092,0.00005227845],"domain_scores_gemma":[0.9993098,0.0002800266,0.00003324642,0.00009751483,0.0002544701,0.00002495966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005033988,0.00005588699,0.0007616279,0.0008732597,0.00002400994,0.0008988207,0.0008107641,0.001517963,0.01632233,0.1002399,0.06450113,0.813944],"study_design_scores_gemma":[0.000006831539,0.00005422104,0.0009348991,0.0002129669,0.00001298456,0.001911664,0.0001909837,0.004051116,0.02003976,0.01305107,0.9594898,0.00004375883],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005710976,0.0200606,0.686847,0.002599427,0.003226772,0.0003416075,0.0009058579,0.005835916,0.2744718],"genre_scores_gemma":[0.08449108,0.03108113,0.6607074,0.00497408,0.001635083,0.0004699604,0.001618111,0.001302131,0.213721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01641039,"threshold_uncertainty_score":0.0548982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108231896057832,"score_gpt":0.2083756136998941,"score_spread":0.1975524240941109,"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."}}