{"id":"W4245167715","doi":"10.1353/scp.0.0082","title":"Academic Search Engine Optimization ( ASEO ): Optimizing Scholarly Literature for Google Scholar & Co.","year":2010,"lang":"en","type":"article","venue":"Journal of Scholarly Publishing","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Class (philosophy); Computer science; Information retrieval; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["scholarly_communication"],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["scholarly_communication"],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.01393522,0.0003884184,0.0006024827,0.002042351,0.0007692972,0.08674648,0.005178427,0.000913442,0.00002925353],"category_scores_gemma":[0.01680941,0.0003558899,0.0004369713,0.002537014,0.00006737978,0.3595777,0.0005403965,0.01465106,0.000007667641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000266577,"about_ca_system_score_gemma":0.000482619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004997771,"about_ca_topic_score_gemma":0.000002539399,"domain_scores_codex":[0.9949989,0.0002992934,0.001349298,0.0006492534,0.001953929,0.0007493578],"domain_scores_gemma":[0.9893008,0.0005157547,0.001191808,0.001019749,0.007360975,0.0006109401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003206318,0.0006311938,0.008652884,0.0002817841,0.0008000942,0.0002091855,0.007163242,0.1123445,0.5295895,0.05639452,0.03747337,0.2461391],"study_design_scores_gemma":[0.01029006,0.001502727,0.002966792,0.002829852,0.0004789938,0.003418694,0.000706518,0.2671232,0.119054,0.05032774,0.5371332,0.004168151],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02974896,0.001751791,0.9595239,0.007191427,0.0007541754,0.0003295258,0.00001628012,0.0002660563,0.0004178634],"genre_scores_gemma":[0.07053082,0.0002815737,0.9267477,0.0009182465,0.001116074,0.00001536618,0.00003775548,0.00006135825,0.00029104],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4996599,"threshold_uncertainty_score":0.9998893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03106641933806783,"score_gpt":0.3222108468978427,"score_spread":0.2911444275597749,"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."}}