{"id":"W4409359845","doi":"10.1139/as-2024-0074","title":"The key role of nitrogen in boosting algal growth in Arctic rivers","year":2025,"lang":"en","type":"article","venue":"Arctic Science","topic":"Marine and coastal ecosystems","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Kvantum-instituutti, Oulun Yliopisto; Koneen Säätiö; Academy of Finland","keywords":"Key (lock); Arctic; Nitrogen; Environmental science; Boosting (machine learning); The arctic; Oceanography; Ecology; Biology; Chemistry; Computer science; Geology; 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":[0.001056362,0.00006338041,0.0001015353,0.0001760373,0.0001616036,0.00005254673,0.0004878456,0.0000174591,0.00005638982],"category_scores_gemma":[0.0005710297,0.00004521875,0.00002225813,0.001379828,0.0002826333,0.0002197269,0.00005713826,0.0001038796,0.00001910444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001545163,"about_ca_system_score_gemma":0.0002082702,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06053722,"about_ca_topic_score_gemma":0.03677137,"domain_scores_codex":[0.9989564,0.00004974178,0.0002158581,0.0002055742,0.0002609385,0.0003114977],"domain_scores_gemma":[0.9993616,0.0003407751,0.00005559514,0.0001435824,0.00005381737,0.00004462989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009516893,0.000005816895,0.9793854,0.00001556638,0.000001387188,0.000002704701,0.0002241157,0.0001720312,0.00007431845,0.001326115,0.00000276516,0.01878024],"study_design_scores_gemma":[0.0001611342,0.0000351559,0.9467172,0.00008214005,0.000002554356,0.000004405844,0.001043671,0.02186104,0.0004385705,0.02936756,0.0002194669,0.00006709102],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9592039,0.0002614621,0.000004894142,0.0002408961,0.0001501381,0.0001136368,0.00000197625,0.000006149743,0.04001697],"genre_scores_gemma":[0.9997179,0.00002563353,0.0001074384,0.00007376653,0.000008722976,0.000001364909,9.647572e-7,6.893302e-7,0.00006351171],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04051403,"threshold_uncertainty_score":0.980805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00571116368981819,"score_gpt":0.1928803383970372,"score_spread":0.187169174707219,"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."}}