arXiv:2602.01496v2 Announce Type: replace
Abstract: The NBSS (normalized biomass size spectrum) is a common, intuitive approach for the study of natural ecosystems. However, very few studies have been dedicated to verifying possible flaws and paradoxes in this widely used method. Evident points of concern of the NBSS method are 1.) the loss of variability due to binning and 2.) the use of intriguing non-biomass units (such as abundance units) on biomass spectra. The main objectives of this study were to verify, test and analyze the procedures involved in transformations that lead to the NBSS plot, and to check for the correctness of currently used units, while testing the hypothesis that NBSS indeed represents biomass, not abundance or biomass flux (dB/dM), while developing i.) a new conceptual framework, ii.) new terminology, iii.) a novel back-transformation method, iv.) high-resolution kernel density estimation (KDE) plots of the density distribution shape, and v.) a new calculation method for numerical values, dimensions, and units. Extensive tests with in situ and synthetic (simulated) data were used to compare the original biomass distributions with binned outputs. Original biomass units and dimensions are retained in the proposed robust ‘bootstrapped, backtransformed, and normalized biomass spectrum’ (bNBS). The combination of quantitative binning and non-parametric KDE intends to address the importance of intuitive, high-resolution, simple plotting methods and the relevance of avoiding binning artifacts and oversimplifications. If a standardized binning vector and units are used, the proposed bNBS may allow for a new approach of robust size spectra science, that allows for quantitative inter-comparisons of biomass across regions and time periods.
Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
arXiv:2603.25006v1 Announce Type: cross Abstract: Early detection of rice leaf diseases is critical, as rice is a staple crop supporting a substantial share of the


