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Microfossils of fish teeth and denticles, referred to as ichthyoliths, provide critical information for depositional ages, paleo‐environments, and marine ecosystems, especially in pelagic realms. However, owing to their small size and rarity, it is time‐consuming and difficult to analyze large numbers of ichthyoliths from sediment samples, limiting their use in scientific studies. Here, we propose a method to automatically detect ichthyoliths from microscopic images using a deep learning technique. We applied YOLO‐v7, one of the latest object detection architectures, and trained several models under different conditions. The model trained under appropriate conditions with an original data set achieved an F1 score of 0.87. We then enhanced the data set efficiently using the pre‐trained model. We validated the practical applicability of the model by comparing the number of ichthyoliths detected by the model with those counted manually. This revealed that the best model can predict the number of triangular teeth, denticles and irregularly shaped teeth with minimal human intervention. This object detection method can extend the applicability of deep learning to a wider array of microfossils and has the potential to dramatically increase the spatiotemporal resolution of ichthyolith records for applications across disciplines.

 

A bstract We present the first measurement of the branching fraction of the singly Cabibbo-suppressed (SCS) decay $$ {\Lambda}_c^{+} $$ Λ c + → pη ′ with η ′ → ηπ + π − , using a data sample corresponding to an integrated luminosity of 981 fb − 1 , collected by the Belle detector at the KEKB e + e − asymmetric-energy collider. A significant $$ {\Lambda}_c^{+} $$ Λ c + → pη ′ signal is observed for the first time with a signal significance of 5.4 σ . The relative branching fraction with respect to the normalization mode $$ {\Lambda}_c^{+} $$ Λ c + → pK − π + is measured to be $$ \frac{\mathcal{B}\left({\Lambda}_c^{+}\to p\eta^{\prime}\right)}{\mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right)}=\left(7.54\pm 1.32\pm 0.73\right)\times {10}^{-3}, $$ B Λ c + → pη ′ B Λ c + → pK − π + = 7.54 ± 1.32 ± 0.73 × 10 − 3 , where the uncertainties are statistical and systematic, respectively. Using the world-average value of $$ \mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right) $$ B Λ c + → pK − π + = (6 . 28 ± 0 . 32) × 10 − 2 , we obtain $$ \mathcal{B}\left({\Lambda}_c^{+}\to p\eta^{\prime}\right)=\left(4.73\pm 0.82\pm 0.46\pm 0.24\right)\times {10}^{-4}, $$ B Λ c + → pη ′ = 4.73 ± 0.82 ± 0.46 ± 0.24 × 10 − 4 , where the uncertainties are statistical, systematic, and from $$ \mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right) $$ B Λ c + → pK − π + , respectively.