Matches in Nanopublications for { ?s ?p <https://doi.org/10.18710/OJC4TH> ?g. }
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- NIRS%20has%20a%20high%20predictive%20power%20%28RMSEP%20%3C%204.79%29%20for%20LOI%20with%20the%20need%20for%20intercept%20and%20slope%20correction%20for%20new%20cores%20measured%20by%20NIRS. obtainsSupportFrom OJC4TH assertion.
- NIRS%20predictive%20models%20for%20LOI%20are%20robust%20when%20applied%20to%20sediments%20with%20properties%20included%20in%20the%20calibration%20dataset. obtainsSupportFrom OJC4TH assertion.
- The%20applicability%20of%20NIRS%20models%20to%20new%20lakes%20can%20be%20achieved%20with%20only%20linear%20bias%20adjustments%20for%20LOI%20predictions. obtainsSupportFrom OJC4TH assertion.
- NIRS-measured%20raw%20LOI%20values%20reveal%20similar%20patterns%20to%20reference%20LOI%20values%20along%20sediment%20cores%2C%20even%20in%20cases%20with%20low%20LOI%20variability. obtainsSupportFrom OJC4TH assertion.
- A%20subset%20of%20approximately%2020%25%20of%20samples%20analyzed%20with%20traditional%20LOI%20methods%20is%20sufficient%20to%20adjust%20NIRS-measured%20LOI%20values%2C%20reducing%20time%20and%20costs. obtainsSupportFrom OJC4TH assertion.
- Non-linear%20fits%20in%20NIRS%20predictions%20for%20LOI%20occur%20when%20sediment%20properties%20%28e.g.%2C%20CaCO3%20content%29%20are%20outside%20the%20predictive%20ability%20of%20the%20model. obtainsSupportFrom OJC4TH assertion.