{"id":"W6893190523","doi":"10.5281/zenodo.14193443","title":"R scripts for the manuscript: \"Beyond the water: Modeling nature-based restoration potential across aquatic-terrestrial boundaries\"","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Scripting language; Workflow; Prioritization; Distribution (mathematics); Land use","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00577671,0.003418714,0.00200213,0.00247279,0.001090113,0.003404599,0.002995767,0.001493131,0.4700375],"category_scores_gemma":[0.03603406,0.001811713,0.002577973,0.0022071,0.0008505413,0.001837763,0.003061919,0.002201186,0.2710353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001515938,"about_ca_system_score_gemma":0.004377718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00738955,"about_ca_topic_score_gemma":0.008681396,"domain_scores_codex":[0.9978502,0.0006659496,0.0003380973,0.0005816809,0.0004171713,0.0001469849],"domain_scores_gemma":[0.9828277,0.01008664,0.001144214,0.001880926,0.003441778,0.0006187842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003117486,0.00004168803,0.001495975,0.001592468,0.000229073,0.0001534201,0.0001476459,0.002411549,0.001386293,0.004070948,0.9627994,0.02535997],"study_design_scores_gemma":[0.001153362,0.0001142382,0.00564449,0.001060985,0.0003207074,0.0004713151,0.0002081564,0.01644011,0.008754668,0.03380872,0.9317555,0.0002678598],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.002096856,0.0002992384,0.1946908,0.001622162,0.0009433352,0.001476308,0.5696413,0.2140393,0.01519069],"genre_scores_gemma":[0.0165793,0.0004783736,0.3533729,0.001730848,0.000333511,0.01318913,0.3287447,0.2330431,0.05252805],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.4700375,"threshold_uncertainty_score":0.7559272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04085126786367966,"score_gpt":0.2778658944358148,"score_spread":0.2370146265721351,"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."}}