{"id":"W4298327506","doi":"","title":"In-Vivo, integration of microalgal culture in Paris","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Algal biology and biofuel production","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"","keywords":"In vivo; Computer science; Biology; Biotechnology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005245441,0.0008041767,0.0006217589,0.0004724976,0.0007672588,0.001671669,0.0006912012,0.0009495703,0.004659411],"category_scores_gemma":[0.0002550677,0.0003912639,0.0008123175,0.0003838622,0.0006060162,0.0007728342,0.001352384,0.001555794,0.002489232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001485674,"about_ca_system_score_gemma":0.0006073611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005629443,"about_ca_topic_score_gemma":0.007434429,"domain_scores_codex":[0.9992754,0.00008460325,0.00004189551,0.0002639736,0.0002320327,0.000102017],"domain_scores_gemma":[0.9997073,0.00005228695,0.00006718926,0.00007225377,0.00005597109,0.00004486703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001616286,0.00005422431,0.0004856857,0.0000882261,0.00001481766,0.0002591836,0.0002509036,0.0005102438,0.9921544,0.0007958013,0.0002673967,0.004957435],"study_design_scores_gemma":[0.0000198601,0.0004858623,0.007133661,0.00003036699,0.00005900182,0.0003502757,0.0003588459,0.001027824,0.9603375,0.0002291798,0.0299409,0.00002680601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8258372,0.007163981,0.06678928,0.0009774214,0.00109451,0.0003705404,0.002236813,0.001075262,0.09445505],"genre_scores_gemma":[0.9015254,0.002880802,0.02776292,0.0003854924,0.00008500603,0.0002579411,0.002240475,0.0003713229,0.06449061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005629443,"threshold_uncertainty_score":0.01558727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01164676027286473,"score_gpt":0.2322500479127193,"score_spread":0.2206032876398546,"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."}}