{"id":"W2078870110","doi":"10.2316/journal.208.2007.3.208-0909","title":"INSTRUCTIONAL STRATEGIES THAT LEVERAGE MOBILE, HANDHELD COMPUTERS","year":2007,"lang":"en","type":"article","venue":"Advanced Technology for Learning","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Affordance; Leverage (statistics); Mobile device; Computer science; Multimedia; Human–computer interaction; World Wide Web; 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":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004008252,0.0001896331,0.0001934344,0.0005030705,0.0004138877,0.00009955599,0.0007589385,0.0002252318,0.000009279898],"category_scores_gemma":[0.0001370851,0.000207013,0.00007071271,0.0006666714,0.000164556,0.0008401398,0.0001776259,0.0006099679,0.00002254223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001088214,"about_ca_system_score_gemma":0.00008504829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003054948,"about_ca_topic_score_gemma":0.000005587974,"domain_scores_codex":[0.998475,0.00002598564,0.0002533584,0.0005434182,0.0001896691,0.0005125398],"domain_scores_gemma":[0.9988509,0.0003168786,0.00022951,0.0004208132,0.0001252085,0.00005672898],"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":[0.00001448204,0.00003657274,0.004221488,0.00001943386,0.00002243674,0.000005837665,0.0003104609,0.05095703,0.003078379,0.1462756,0.00004746383,0.7950108],"study_design_scores_gemma":[0.003905129,0.00233189,0.02242249,0.0002365409,0.00003194809,0.0006000901,0.008498157,0.05883582,0.06318288,0.182531,0.6555679,0.001856172],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2276312,0.0001528652,0.7688247,0.0003844751,0.0009550874,0.000309432,4.180237e-7,0.001063748,0.0006780977],"genre_scores_gemma":[0.6673493,0.00001220968,0.3321391,0.00005766477,0.00005508264,0.0001103518,0.000007800668,0.00001638994,0.0002520599],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7931546,"threshold_uncertainty_score":0.8441744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009229019861207315,"score_gpt":0.2739790373566257,"score_spread":0.2647500174954183,"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."}}