{"id":"W2065915312","doi":"10.3138/jsp.41.2.176","title":"Academic Search Engine Optimization (<scp>ASEO</scp>)","year":2009,"lang":"en","type":"article","venue":"Journal of Scholarly Publishing","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Search engine optimization; Computer science; Search engine; Information retrieval","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003728942,0.001091304,0.001142151,0.002628902,0.0006777772,0.002792625,0.00103251,0.001112933,0.007738376],"category_scores_gemma":[0.01537127,0.0003764523,0.000929822,0.004158692,0.0008314882,0.002522444,0.001872729,0.001236726,0.002292259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009072268,"about_ca_system_score_gemma":0.002634653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251403,"about_ca_topic_score_gemma":0.00158221,"domain_scores_codex":[0.9966215,0.001385783,0.000248902,0.0003268035,0.001146817,0.0002701667],"domain_scores_gemma":[0.9929579,0.0035894,0.0007384072,0.001192036,0.001292705,0.0002295422],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004225019,0.0005015272,0.005081893,0.0008661883,0.0002165965,0.0001731078,0.0001394629,0.1572573,0.004147465,0.1307856,0.04951646,0.6508919],"study_design_scores_gemma":[0.0001062597,0.0004730351,0.002392764,0.0001674815,0.0001534375,0.0005165094,0.0001668897,0.7922155,0.01255021,0.1186994,0.07250264,0.00005591191],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04458052,0.004578209,0.8304687,0.003545553,0.0006266132,0.001107965,0.001496847,0.005345179,0.1082504],"genre_scores_gemma":[0.4532521,0.002569568,0.5232604,0.0008641634,0.0005703318,0.0006290353,0.001664297,0.001018336,0.01617182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9972074,"threshold_uncertainty_score":0.02588749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02772254798134443,"score_gpt":0.2599938540029307,"score_spread":0.2322713060215863,"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."}}