{"id":"W2592451157","doi":"10.18331/brj2017.4.1.5","title":"Enhanced dark fermentative biohydrogen production from marine macroalgae Padina tetrastromatica by different pretreatment processes","year":2017,"lang":"en","type":"article","venue":"Biofuel Research Journal","topic":"Food Industry and Aquatic Biology","field":"Agricultural and Biological Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Defence Research and Development Organisation; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Biohydrogen; Chemistry; Gallic acid; Food science; Yield (engineering); Substrate (aquarium); Dark fermentation; Nuclear chemistry; Hydrogen production; Biochemistry; Organic chemistry; Biology; Hydrogen; Materials science; Antioxidant","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":[],"consensus_categories":[],"category_scores_codex":[0.0001212204,0.0004745624,0.000287907,0.000221827,0.000138754,0.0002544743,0.0001503914,0.0002360019,0.0004481646],"category_scores_gemma":[0.00008771687,0.0001526185,0.0004090918,0.0002470611,0.0001058686,0.0002195875,0.0002388191,0.0003318992,0.0001262588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001929981,"about_ca_system_score_gemma":0.0002140048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009471118,"about_ca_topic_score_gemma":0.002389785,"domain_scores_codex":[0.9999205,0.0000105725,0.000009990961,0.00001996007,0.00002293679,0.0000160923],"domain_scores_gemma":[0.999967,0.000004732194,0.000009674958,0.000004040345,0.000007836305,0.000006764781],"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":[0.00003764351,0.00001317117,0.0001097781,0.00002836848,0.000002738528,0.00001572299,0.000005422963,0.00004770786,0.9989213,0.000006858197,0.000003941698,0.0008073729],"study_design_scores_gemma":[0.000006245325,0.000291846,0.003778731,0.000004339378,0.00001688158,0.00003733573,0.00002930507,0.0004416419,0.9949448,0.00001430988,0.0004301188,0.000004517406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997777,0.0005601403,0.001038043,0.00003140474,0.000009957069,0.00001548887,0.00009635709,0.00001828268,0.0004533334],"genre_scores_gemma":[0.9950544,0.0006867795,0.003210498,0.00002858885,0.00000327154,0.00001868233,0.0002278708,0.000007055011,0.0007628155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009471118,"threshold_uncertainty_score":0.001883209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07641100682538754,"score_gpt":0.3374663806996069,"score_spread":0.2610553738742194,"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."}}