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A powerful CuCl‐catalyzed sequential one‐pot reaction of aryl methyl ketoxime acetates with cyclic N‐sulfonyl imines followed by elimination in the presence of base is reported. This hydrazine‐free method conveniently makes C−C and N−N bonds via a radical cleavage of the N−O bond, delivering a special class of C3‐hydroxyarylated pyrazoles in good yields. Surprisingly, while employing CuI as a catalyst instead of CuCl, the reaction proceeds through a non‐radical pathway which embodies a new tactic for the high‐yielding access to value‐added conjugated N‐unsubstituted ketimines. Moreover, additive‐free approach to sulfamidate‐fused‐pyrazoles was achieved by successfully catalyzing addition and oxidative N−N bond‐making reactions by CuI and CuCl, respectively. Significantly, our novel technique could convert the prepared ketimines into the pharmacologically recognized 6H‐benzo[c]chromene frameworks. [ABSTRACT FROM AUTHOR]

Seagrass meadows are an essential part of the Great Barrier Reef ecosystem, providing various benefits such as filtering nutrients and sediment, serving as a nursery for fish and shellfish, and capturing atmospheric carbon as blue carbon. Understanding the phenotypic plasticity of seagrasses and their ability to acclimate their morphology in response to environ-mental stressors is crucial. Investigating these morphological changes can provide valuable insights into ecosystem health and inform conservation strategies aimed at mitigating seagrass decline. Measuring seagrass growth by measuring morphological parameters such as the length and width of leaves, rhizomes, and roots is essential. The manual process of measuring morphological parameters of seagrass can be time-consuming, inaccurate and costly, so researchers are exploring machine-learning techniques to automate the process. To automate this process, researchers have developed a machine learning model that utilizes image processing and artificial intelligence to measure morphological parameters from digital imagery. The study uses a deep learning model called YOLO-v6 to classify three distinct seagrass object types and determine their dimensions. The results suggest that the proposed model is highly effective, with an average recall of 97.5%, an average precision of 83.7%, and an average f1 score of 90.1%. The model code has been made publicly available on GitHub (https://github.com/sajalhalder/AI-ASMM). • We constructed an annotated dataset of seagrass objects, employing the image annotation platform Roboflow. • We devised a seagrass multi-object classification model using advanced deep learning technique YOLOv6. • Furthermore, we introduced a dynamic model tailored for quantifying object morphol-ogy, encompassing dimensions such as length. • Our model shows efficacy with 97.5% recall, 83.3% precision, and a 90.1% F1-score on average in experimental results. • Our morphology calculator significantly achieves 78.14% accuracy in measuring the lengths of the three longest objects. [ABSTRACT FROM AUTHOR]