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Trees are an important carbon sink as they accumulate biomass through photosynthesis1 . Identifying tree species that grow fast is therefore commonly considered to be essential for efective climate change mitigation through forest planting. Although species characteristics are key information for plantation design and forest management, feld studies often fail to detect clear relationships between species functional traits and tree growth2 . Here, by consolidating four independent datasets and classifying the acquisitive and conservative species based on their functional trait values, we show that acquisitive tree species, which are supposedly fast-growing species, generally grow slowly in feld conditions. This discrepancy between the current paradigm and feld observations is explained by the interactions with environmental conditions that infuence growth. Acquisitive species require moist mild climates and fertile soils, conditions that are generally not met in the feld. By contrast, conservative species, which are supposedly slow-growing species, show generally higher realized growth due to their ability to tolerate unfavourable environmental conditions. In general, conservative tree species grow more steadily than acquisitive tree species in non-tropical forests. We recommend planting acquisitive tree species in areas where they can realize their fast-growing potential. In other regions, where environmental stress is higher, conservative tree species have a larger potential to fx carbon in their biomass. 

Abstract A new type of radio frequency (RF) timing technique is presented. It is based on a helical deflector, which performs circular or elliptical sweeps of photo- or secondary electrons, accelerated to keV energies, by means of RF fields in the 500–1000 MHz range. By converting a time distribution of the electrons to a hit position distribution on a circle or ellipse, this device achieves extremely precise timing, similar to streak cameras. Detection of the scanned electrons, using a position sensitive detector based on microchannel plates and a delay line anode, resulted in a timing resolution of 10 ps, which can be potentially improved to 1 ps. RF-Timer-based single photon and heavy ion detectors have potential applications in different fields of science and industry, which include high energy nuclear physics and imaging technologies. This technique could play a crucial role in developing of sub 10 ps Time-of-Flight Positron Emission Tomography. 

Abstract The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments. 

The EIC Comprehensive Chromodynamics Experiment (ECCE) detector has been designed to address the full scope of the proposed Electron Ion Collider (EIC) physics program as presented by the National Academy of Science and provide a deeper understanding of the quark–gluon structure of matter. To accomplish this, the ECCE detector offers nearly acceptance and energy coverage along with excellent tracking and particle identification. The ECCE detector was designed to be built within the budget envelope set out by the EIC project while simultaneously managing cost and schedule risks. This detector concept has been selected to be the basis for the EIC project detector.