{"id":"W5766521","doi":"10.1007/978-3-642-23771-3_31","title":"e-Rural: A Framework to Generate Hyperdocuments for Milk Producers with Different Levels of Literacy to Promote Better Quality Milking","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"E-Learning and Knowledge Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Literacy; Computer science; Quality (philosophy); Production (economics); Population; Milking; Agricultural science; Pedagogy; Sociology; Geography; Economics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001133585,0.000671648,0.000797473,0.0008174363,0.0002507235,0.0005187942,0.003167067,0.0001985987,0.00001418927],"category_scores_gemma":[0.0001669648,0.0005184441,0.0001491269,0.0007002943,0.0002838252,0.0003831304,0.001825948,0.0005494163,0.00002199094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002577056,"about_ca_system_score_gemma":0.0001856666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003200991,"about_ca_topic_score_gemma":0.00003414789,"domain_scores_codex":[0.9955302,0.00007322046,0.0007320382,0.001859553,0.000960394,0.0008445464],"domain_scores_gemma":[0.9966477,0.0002826256,0.0003941646,0.001826148,0.0005522581,0.0002971355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005181812,0.00008422304,0.000330869,0.0002660195,0.00005776314,0.0000132469,0.01096459,0.006530981,0.0004400852,0.01253112,0.00003192745,0.9686974],"study_design_scores_gemma":[0.004028575,0.01043732,0.01658904,0.03001375,0.0002760554,0.0000702385,0.00001096036,0.1596966,0.1097709,0.6194704,0.03836719,0.01126899],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01443346,0.0001186998,0.979907,0.001986381,0.00139317,0.001693623,0.00001138424,0.0001124356,0.0003438603],"genre_scores_gemma":[0.2827948,0.000003736663,0.7136638,0.002331325,0.0003763305,0.00008180162,0.000002742252,0.00004972611,0.0006957],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9574283,"threshold_uncertainty_score":0.9997267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03575364879928548,"score_gpt":0.2957988712983656,"score_spread":0.2600452224990801,"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."}}