dataesr/scientific-paragraphs-categorization huggingface dataset:
This is reflected in the relatively higher aggregated scores for carbon cycle variables and functional relationships in ELMv1-CNP. Figure  2  shows the global net ecosystem carbon balance metric in ILAMB for ELMv1-CNP and ELMv1-CN. The observational datasets for this metric are from the Global Carbon Project (Fig. 2a ) (Le  Quéré et al. , 2016)  and from the inversion-based estimate  (Hoffman et al. , 2014) , both providing global totals of land carbon accumulation but for different historical time periods (1850-2010 for  Hoffman et al. , 2014 , and 1959 -2010 for Le Quéré et al. , 2016). The simulated global C balance by both ELMv1-CNP and ELMv1-CN is in the range of uncertainty of observational estimates, with ELMv1-CNP-simulated historical global carbon accumulation being a better match with mean observational estimates, particularly after 1950.
ELMv1-CN estimated a net accumulation of land carbon of 22 Pg C over the period 1850-2010, which is much higher than the mean observational estimate of -8 Pg C. ELMv1-CNP estimated a land carbon accumulation of 7 Pg C. Figure  3  shows the spatial distribution of vegetation biomass for the benchmark data and model bias in ILAMB. Overall both ELMv1-CN and ELMv1-CNP tend to overestimate biomass, compared to this specific global product of biomass (GEOCARBON). The high bias in the tropical region is much reduced in ELMv1-CNP simulations (Fig. 3a,   b  and c ). The better performance of ELMv1-CNP is also reflected in the spatial Taylor diagram for biomass (Fig. 3d ). Another important benchmark in ILAMB is the functional relationships between two variables, for example the relationship between GPP and precipitation and the relationship between annual mean leaf area index (LAI) and precipitation.
For example,  Quesada et al. (2012)  found that the decreasing west-east gradient in productivity is mostly related to total soil P across the Amazon basin. Yang et al. (2019)  showed that consideration of soil P availability improved model-simulated productivhttps://doi. org/10. 5194/bg-20-2813-2023 Biogeosciences, 20, 2813-2836, 2023   1. ity, enabling the model to capture the productivity gradient from west to east across the Amazon basin. The problem is that this productivity gradient across the Amazon basin is not captured in ILAMB benchmark data so the failure of a CN model would not be captured by ILAMB. We show that the model performance generally improved with realistic P availability through the implementation of a prognostic P cycle in ELM. Compared to ELMv1-CN, ELMv1-CNP-simulated biomass has lower bias across the tropical regions as P limitation leads to lower productivity and hence lower biomass.
ELMv1-CNP produces better IL-AMB scores on the functional relationships between GPP, LAI, and other forcing variables, mainly due to improved estimates of GPP and LAI in tropical regions. ELMv1-CNP also produces higher ILAMB scores for the integrated benchmarks such as global net ecosystem carbon balance and carbon dioxide concentration. We note that satisfactory performance for these two integrated metrics is most critical to a land model in ESMs as they are most relevant to the coupling between land ecosystems and radiatively forced climate change. ELMv1-CNP is not always better than ELMv1-CN from the benchmarks in the current ILAMB system. One of the benefits of a multi-metric analysis package like ILAMB is that we can compare performance at different levels of granularity, and it is rare that any one model has uniformly improved performance over any other single model on every fine-grained metric.
For example, the site-level evaluations in ILAMB do not take into account site-specific disturbance histories, which can be an important driver of NEE variability over time at a given site. Although the ILAMB benchmarking system is very useful for evaluating model performance from different aspects simultaneously, interpretation of ILAMB scores deserves extra caution with known observational bias considered. For example, ILAMB uses LAI estimated from remote sensing observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) as benchmarking data, while studies have suggested that MODIS LAI may be biased low due to reflectance saturation in dense canopies in the tropical forests  (Shabanov et al. , 2005; Huete et al. , 2002; Kobayashi and Dye, 2005). Another example is the observational data for biomass.
There are significant differences between the tropical dataset and the GEOCARBON dataset for tropical biomass, but they were given about the same default weight in the ILAMB scoring system. Mitchard et al. (2014)  investigated the marked differences between different estimates of Amazon biomass and suggested the regional biases in some remote sensing products might be due to the lack of consideration of ecological variation in tree wood density and allometry. Further investigation of these datasets is needed to ensure the quality of biomass benchmarking data. The current version of ILAMB includes the analysis of 28 variables using more than 60 datasets or data products. None of these variables, however, are directly related to nutrient cycles. As more land surface models are implementing N and P dynamics, it is becoming increasingly important to include metrics for nutrient stocks and fluxes.
Davies-Barnard et al. (2020)  assessed five nitrogen-enabled land surface models in CMIP6 and called out the need to have better constraints of nitrogen cycle processes. The need is equally urgent, if not more, to synthesize more observations to better constrain the P cycle processes, as less synthesized data are available for P. Encouragingly, recent studies have started to develop observational datasets based estimate of N and P cycling on the global scale for model evaluation, such as the GOLUM-CNP dataset we used in this study. We hope to highlight the need and engage the broader community in developing additional nutrient datasets that can be included in ILAMB. Other metrics that would be useful are the responses from N and P addition experiments. As  Yang et al. (2014)  showed, fertilization experiments at sites along the Hawaii chronosequence provided a useful evaluation test bed to assess modelsimulated responses to N and P fertilization effects.
The increased sensitivity of GPP and NPP to CO 2 enrichment in ELMv1-CNP, compared with the predecessors CLM4 and CLM4. 5, will very likely reduce the bias in the atmospheric fraction of human CO 2 emissions in previous coupled simulations as noted by  Hoffman et al. (2014). In fact, CO 2 concentration metrics in ILAMB, which translate https://doi. org/10. 5194/bg-20-  2813 -2023 Biogeosciences, 20, 2813 -2836 , 2023  model-simulated NEE into atmospheric CO 2 signal using an atmospheric transport model  (Collier et al. , 2018) , are intended for the evaluation of this sensitivity. The inferred atmospheric CO 2 concentration from ELMv1 is very reasonable compared with observed NOAA flask data (Figs. S7 and S8). 4. 4 Model-estimated carbon, nitrogen, and phosphorus pools and fluxes Global C, N, and P pools in our ELMv1-CNP simulation are in good agreement with recent independent global estimates, indicating that ELMv1-CNP is capable of simulating the contemporary C, N, and P cycles.
, 2010; Goll et al. , 2012; Yang et al. , 2014 Yang et al. , , 2019)). However, this extraction method is time-consuming and challenging, and not many routine measurements have been made using this technique. As such, observational estimates of P pools and fluxes are extremely limited. Although recent global Hedley database development  (Yang and Post, 2011; Hou et al. , 2018)  has been helpful in global model development and evaluation, more observational data on P stocks and fluxes are needed to better constrain P-enabled models. 4. 5 Effects of accounting for the P cycle dynamics on simulated carbon balance.
Evaluating global C, N, and P pools and fluxes in the context of literature values suggests that ELMv1-CNP provides a reasonable representation of contemporary globalscale C, N, and P cycles. We highlight the data needs for global land model evaluation, particularly the need for more synthesis datasets on nutrient pools and fluxes, as well as observations from manipulation experiments that provide additional benchmark data for nutrient cycle evaluation. This need is becoming increasingly pressing as more land models are including N and P cycle dynamics and C-N-P interactions. We also identify challenges in constraining P cycle dynamics and point to the need for soil P measurements. Our simulations suggest, probably not surprisingly, that in general P is the more limiting nutrient in the tropical regions while N is more limiting in the middle to high latitudes.
Acknowledgements. This research was supported as part of the  Energy Exascale Earth System Model (E3SM)  project, funded by the  Office of Biological and Environmental Research, Office of Science, U. S. Department of Energy. This research was also supported by the  Oak Ridge National Laboratory's (ORNL) Terrestrial Ecosystem Science Focus Area (TES SFA). Xiaojuan Yang  was also supported as part of the  Next Generation Ecosystem Experiments-Tropics  project. Forrest Hoffman  was supported by the  Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA ), which is sponsored by the  Regional and Global Model Analysis (RGMA) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER)  in the  U.
For this reason the rejection of this background contribution and the study of its impact on the systematic errors is very important in JUNO. This is the main role of the Top Tracker. All OPERA experiment  [4]  Target Tracker  [5]  plastic scintillating modules have been recovered and sent to China where they will be used to form the JUNO Top Tracker. In the OPERA experiment, the Target Tracker modules were shielded by the emulsion/lead bricks, while in the JUNO cavern the TT will be directly exposed to rock radioactivity. On top of that, the radioactivity in the JUNO cavern is measured to be about two orders of magnitude higher than the one observed in the underground Laboratori Nazionali del Gran Sasso (LNGS).
Details on the operation modes of the Top Tracker are detailed in Sec. 6, which is followed by a description of a prototype of the Top Tracker in Sec. 7 where these modes were extensively tested. Elements concerning the supporting structure for the Top Tracker modules and installation are provided in Sec. 8. Long term stability monitoring of the Top Tracker modules is presented in Sec. 9. Finally, a summary and concluding remarks are presented in Sec. 10.
During the prospecting of the JUNO site, rock samples were collected to determine the content of radioisotopes. It was determined 2  that the rock radioactivity at the JUNO site is about two orders of magnitude higher than that in the LNGS where the detector had been previously used, as shown in Tab. 1. In addition to the higher radioactivity from the cavern, in JUNO the TT will also be directly exposed to the rock, while the OPERA TT modules were shielded by lead/emulsion bricks. The MA-PMT trigger rate induced by the rock radioactivity is estimated using the JUNO official simulation software  [6] ,   where the TT modules are placed as previously described. In this study decays were individually simulated from the surrounding rock (within a 50 cm thickness from the rock surface) and the decay particles are then propagated using GEANT4  [7].
5 , to about 2 kHz, as shown in Fig. 6 , which can be handled by the acquisition system. The two levels of time coincidence described correspond to the L1 and L2 triggers. The L1 trigger relates to the time coincidences within a single wall while the L2 trigger does the same at the level of the entire TT. The implementation of these coincidence criteria in the electronics is discussed in the following sections. Total Event Rate (Hz) L1: XY , L2: Align radioactivity Figure  6 : Total event rate of the full detector at various trigger levels, from the MA-PMT trigger rate up to the rate after events are reconstructed by the Top Tracker offline software, due to radioactivity in the surrounding rocks (green) and due to atmospheric muons (blue). The L1 selection criteria requires an X and Y coincidence involving 3 out of 4 MA-PMTs at the end of the corresponding modules in the perpendicular X and Y directions.
The L2 selection criteria requires the L1 coincidences to be on 3 walls of the Top Tracker located on different layers. It is worth noting that in Fig. 6  were chosen the L1 and L2 algorithms that are the most likely to be used in the Top Tracker, however the final decision has not yet been made. The L1 trigger, which is discussed in Sec. 4. 3, provides about an order of magnitude reduction of the rate by requiring 100 ns coincident MA-PMT triggers in the two perpendicular planes of any given wall, and with at least one of the planes having the MA-PMTs on both sides of the module triggered. The L2 trigger, which is discussed in Sec. 4. 4, provides another reduction factor of about 400 to the event rate by requiring at least 3 aligned walls on different layers of the detector to have produced a L1 trigger within a 300 ns sliding time window.
In case an alternative algorithm is used in the L1 and L2 electronics cards, due to difficulty in implementing them, and the data reduction is not as efficient as discussed here, it is foreseen that the data acquisition system will be able to apply online additional criteria to reduce it to the rate described above. It is also useful to note, as shown in Fig. 6  that the radioactivity rate can be further reduced offline by performing a full 3D reconstruction of the muon track, rather than just requiring walls to be aligned. This reconstruction is presented in Sec. 5 and corresponds to the "offline reco" level on the aforementioned figure, for which only a 68% upper limit is shown for radioactivity. Any potential remaining radioactivity induced events after the reconstruction can still be further reduced by requiring a correlated signal between the Top Tracker and the central detector or the water Cherenkov veto detector, after that the impact of the radioactivity becomes negligible compared to the atmospheric muon rate.
The GTB sends back a validation or a rejection signal to the CBs that, correspondingly, either sends the related data to the JUNO data acquisition system or issues a reset signal to the relevant ROBs in about 1 µs. Both trigger levels are essential to significantly reduce the TT data rate, as discussed previously, and to reduce the detector dead time of the FEB MAROC3. The FEB and ROB are installed in each module end-cap. The FEB is connected to the MA-PMT that observes the light collected by WLS fibres in the 64 scintillator strips of each module and to the ROB. The ROB is connected to the FEB and to the CB of that specific wall. The CB is located at the centre of the wall and is connected to all 16 ROBs of the wall. The CB is also connected to the white rabbit (WR) switch, to the data acquisition system (DAQ) and to the GTB.
Front End Board (FEB) The FEB is an 8-layer PCB, 3 cm × 13 cm in size, carrying the MAROC3 and a small Spartan6 FPGA used to serialise the hit signals from 64 parallel outputs to 8 serial links. This operation allows the ROB to obtain the information of which channels have triggered, which is then used for online zero suppression if that is the set acquisition mode. The board is directly plugged to the MA-PMT, as shown in Fig. 9. The lines from the MA-PMT to the MAROC3 inputs are well separated and protected from external noise sources by four ground planes in the PCB. The readout electronics of the Top Tracker is based on a 64-channel ASIC, the MAROC3 designed by the Omega laboratory  [8]. The chip is provided in a 12 mm × 12 mm TFBGA package with 353 pins. The main characteristics of this chip are: • Configurable gain compensation (called gain equalisation factor in the following, g.
e. f. ), varying from 0 to 3. 9, to equalise the anode-to-anode gain differences of the MA-PMT channels  [5]. The MAROC3 is equipped with an adjustable gain system incorporated in the preamplifier stage delivering an identical amplification factor to both, the fast shaper (trigger arm) and slow shaper (charge measurement arm), of each channel. In particular the CB is connected to the L2, to the white rabbit (WR) switch via UDP and to the data acquisition system via TCP. Next to the CB in the wall is also located the split-power board that distributes the power for all ROBs and the CB in each wall. • Delivery of a global low noise auto-trigger with 100% trigger efficiency for minimum ionising particles, using a threshold as low as 1/3 p. e. , corresponding to 53 fC for a total gain of 10 6.
The test pulse generators, illuminating the fibres in the endcaps of the TT modules, are the same used in OPERA  [5]. The generator produces short pulses, 20 ns wide with adjustable amplitude, powering blue LEDs which excite the WLS fibres exiting the scintillator strips. In the calibration procedure the pulse amplitude is set in such a way to simulate the single photoelectron signal on the MA-PMT while the HOLD signal is issued with an adjustable delay with respect to the LED driver pulses, emulating the acquisition of standard signals. Pedestals can be measured with random emission of HOLD signals inside the ROB. These various data acquisition modes will be further described in Sec. 6. At nominal working conditions on a detector prototype, an operating current of 350 mA has been measured at the nominal operating voltage of 12 V supplied to the ROB, powering also the FEB and the MA-PMT.
TT operation modes There are four different modes foreseen for the TT operation. These modes were thought to make it possible to monitor the TT stability, calibrate it and perform data acquisition. Each of those modes can also have additional configurable options for added flexibility, while keeping the same global goal. The four modes foreseen, which will be described in details in the following subsections, are the trigger rate test (TRT), pedestal measurement (PED), LED light injection (LED), and normal data taking modes. Tab. 2 shows some characteristics for these four modes. This mode is used to measure the trigger rate per channel. The main goals of this mode are to characterise the electronic noise, define the trigger threshold and search for light leaks in the TT modules. In the TRT mode, the acquisition is set in auto-trigger mode without charge acquisition, only the number of triggers in a given time window (typically 1 s) for each channel is returned.
It is also foreseen that the trigger rate of all channels will be monitored during data taking automatically, in addition to this acquisition mode. The main advantage of having the possibility to request this data is to reduce the latency in this measurement for direct usage during the above-mentioned calibration and search for light leaks. Fig. 21  shows an example of the determination of the threshold and electronic noise for one particular channel of the FEB using multiple acquisitions using the TRT mode with a varying threshold set in the FEB. For the determination of the electronic noise no charge is injected, while for the determination of the threshold a known charge (corresponding to 1/3 p. e. ) is injected in the tested channel. Both measurements have been done, using a test setup, for all channels of all FEB cards to validate the FEB production.
LED light injection mode This mode works similarly to the Pedestal mode described before, however the measurement happens at about the same time of the light injection. As in the Pedestal mode, in the LED mode data is taken with a fixed trigger frequency, with which the LED is pulsed ignoring the MA-PMT trigger. The light injected in the system uses the same system described in  [5]. In this system, light is injected at each end-cap into the WLS fibres just in front of the fibre-MA-PMT optocoupler with the help of two blue LEDs, straight PMMA light guides and a white painted diffusive box as shown in Fig. 24. The LEDs are pulsed by a signal from the ROB, which is equipped with a driver designed for this application, as described previously. After a certain configurable delay from the LED pulse, to account for the time required to flash the LED, an external trigger is generated to perform the charge measurement in the MAROC3.
Normal run mode This mode is the one used for regular data acquisition. In this case the MA-PMT signal passing the threshold will produce a MAROC3 trigger as described beforehand. Addition-  ally, zero-suppression in the ROB can be enabled in this mode to reduce the data size. To safeguard against unexpectedly high rates, it is also possible to entirely disable the charge readout, which reduces the detector dead-time and significantly decreases the data size, while keeping the information about the triggered channels. A mixed acquisition option with and without charge readout is being investigated. In this case, a new trigger will start charge readout if the acquisition system is not already performing the charge readout for another trigger. However, in the case where the acquisition system is busy, no charge readout will be performed and only the triggered channels and trigger time will be saved.
In each of the 8 modules end-caps was installed one MA-PMT, one FEB, and one ROB. All the 8 ROBs are connected to one CB placed in the metal frame supporting the prototype. In the prototype it is also possible, for testing purposes, to read the data directly from the ROB without passing by the CB, by using a different version of the ROB specifically designed for that purpose. Using the prototype, it has already been possible to validate the calibration procedures and test the interface between the various electronics cards using natural signals, albeit with smaller rate than the one expected in JUNO, due to the lack of the high rate of events expected from the rock radioactivity. It is worth noting that most figures in Sec. 6 were obtained from data of this prototype. Using the TT prototype several consecutive days of data have been collected.
In addition to that, the "tables" of the three walls above the chimney have a different design providing a separation by only 21 cm in their vertical position. During the installation, the position of the walls will be measured by survey instruments with an accuracy of about 1 mm. The relative alignment of the walls can be further verified with the help of atmospheric muons during the data taking. According to the installation schedule, one wall per day will be assembled and mounted in the detector. This time includes some time for curing the modules of any light leaks that might have appeared due to transportation or storage conditions.
Time difference (ns) Figure  31 : Time difference between the first and last layers of the prototype from atmospheric muons when the prototype is in vertical position. tor will occur before the TT will be used in JUNO experiment, and will continue during the whole lifetime of the detector. From August 2006 until April 2013 electronic cards of the OPERA experiment were almost continuously registering atmospheric muons along with muons produced in ν µ CC interactions during the OPERA beam (CERN Neutrinos to Gran Sasso, CNGS) runs. Over the entire period of the data taking, more than 10 6 straight muon tracks were reconstructed in the Target Tracker detector. Those tracks were used, in particular, to measure the TT module detection efficiency and to monitor the stability of the TT response to minimum ionising particles.