dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  CB05 Sarwar, Luecken, Yarwood, Whitten, & Carter, 2008 and RACM2 Goliff, Stockwell, & Lawson, 2013)  for accurate estimation of inorganic concentrations. GENOA is also a user-parameterized algorithm, allowing users to design their own reductions by adjusting reduction parameters and options. One example of customization is the selection of evaluation datasets. An evaluation dataset is a set of atmospheric conditions for evaluating candidate reductions (see Section 2. 4. 2). By selecting the appropriate evaluation dataset, users can train mechanisms for specific purposes, such as focusing on SOA modeling in urban areas. Mechanisms trained under specific conditions are expected to be more compact than those trained under general atmospheric conditions with the same level of accuracy. To facilitate customization, the training process can be divided into several sequential stages, referred to as training stages.
Parallel reduction Fig. 2  illustrates the series reduction adopted in GENOA v1. 0 and the parallel reduction in GENOA v2. 0. In contrast to the series reduction, which tests one candidate reduction per step and accepts it if the accuracy criteria are met, GENOA v2. 0 investigates multiple candidate reductions simultaneously and selects the one with the highest reduction score (see Section 2. 3. 4). With this parallel reduction framework, GENOA v2. 0 searches all competing candidate reductions related to the targeted species or reactions using all reduction strategies at each reduction step. Afterward, the candidate reductions are examined under the applied evaluation dataset and different sets of initial SOA precursor concentrations. The best reduction is then selected as the starting point for the next reduction.
Reduction score Total SOA concentrations are simulated to evaluate the performance of the candidate reductions under the applied evaluation dataset. Simulations are conducted for five days, starting at midnight and noon, in order to account for both daytime and nighttime chemistry. To estimate the uncertainty induced in the current mechanism by the candidate reduction, a reduction error (𝜖) is computed based on the total SOA mass. This error is defined as the larger value of the fractional mean error (FME) between the day 1 and day 2-5 simulation results. The FME is computed according to Eq. (  1 ), where C 𝑣𝑎𝑙,𝑖 and C 𝑐𝑚𝑝,𝑖 are the SOA mass concentrations at time step i simulated with the current mechanism and the mechanism used for comparison, respectively. Here, this comparison mechanism is either the original mechanism after prereduction (see Section 2.
4. 1) or the mechanism with the latest approved candidate reduction (noted as the previously reduced mechanism). Reduction errors are classified as 𝜖 𝑟𝑒𝑓 and 𝜖 𝑝𝑟𝑒 , where the comparison mechanisms considered are the mechanism after prereduction and the previously reduced mechanism, respectively. The reduction error (𝜖) is restricted by the predefined error tolerance (𝜀). During the training process, the average and maximum reduction errors (𝜖 𝑟𝑒𝑓 and 𝜖 𝑝𝑟𝑒 ), as well as the differences of 𝜖 𝑟𝑒𝑓 induced between current and previously reduced reductions (𝛿𝜖) are restricted for each candidate reduction. For clarity, errors (𝜖) with subscripts ''ave'' and ''max'' represents the average and maximum values under the evaluation dataset, while the superscripts ''ref'' and ''pre'' detail the mechanism used to compute the error (original reference after prereduction or previously reduced).
A variety of error tolerances may be used to evaluate training at different stages. The error tolerance setting employed in this study for each stage can be found in Section 2. 4. 4 and Table  4. 𝐹 𝑀𝐸 = 2 ∑ 𝑖=1 𝑛 𝑎𝑏𝑠(𝐶 𝑣𝑎𝑙,𝑖 -𝐶 𝑐𝑚𝑝,𝑖 ) 𝑛 ∑ 𝑖=1 𝑛 (𝐶 𝑣𝑎𝑙,𝑖 + 𝐶 𝑐𝑚𝑝,𝑖 ). (1) With parallel reduction, several candidate reductions can respect the prescribed error tolerances. Therefore, to determine the most appropriate reduction, a reduction score (S 𝑟𝑑𝑐 ) is calculated in GENOA v2. 0 using Eq. (  2 ). The terms 𝑁 𝑛𝑜𝑤 and 𝑁 𝑝𝑟𝑒 denote the numbers in the current and previously reduced mechanisms, and the subscripts ''rcn'', ''sps'', and ''aero'' represent reaction, species, and condensables respectively. The reduction score provides a measure of the effectiveness of the candidate reduction in decreasing the number of reactions (𝑁 𝑟𝑐𝑛 ), species (𝑁 𝑠𝑝𝑠 ), and condensables (𝑁 𝑎𝑒𝑟𝑜 ).
In order to emphasize the importance of reducing aerosols, which impose the greatest computational burden in 3-D modeling, the term 𝑁 𝑎𝑒𝑟𝑜 in S 𝑟𝑑𝑐 is multiplied by ten (i. e. , the typical number of aerosol size bins used in CHIMERE). The reduction candidate that satisfies the error tolerances and achieves the highest reduction score is deemed the best reduction and is accepted for further training. 𝑆 𝑟𝑑𝑐 = (𝑁 𝑛𝑜𝑤 𝑟𝑐𝑛 -𝑁 𝑝𝑟𝑒 𝑟𝑐𝑛 ) + (𝑁 𝑛𝑜𝑤 𝑠𝑝𝑠 -𝑁 𝑝𝑟𝑒 𝑠𝑝𝑠 ) + (𝑁 𝑛𝑜𝑤 𝑎𝑒𝑟𝑜 -𝑁 𝑝𝑟𝑒 𝑎𝑒𝑟𝑜 ) × 10 (2) 2. 4. Application to monoterpene SOA reduction GENOA v2. 0 is applied to the reduction of the monoterpene SOA mechanisms. The reference mechanism that serves as a starting point and benchmark for reduction is described in Section 2. 4. 1. Details on the evaluation dataset, which includes the atmospheric conditions used for reduction evaluation, can be found in Section 2.
, 2019). Consequently, the reference mechanism adopted for the reduction is constructed by combining the reduced PRAM and the MCM -MT mechanisms, followed by a prereduction process. For simplicity, the reduced PRAM is hereafter referred to as ''PRAM''. To build a numerically stable and faster reference mechanism, a prereduction process is carried out as follows: • Combine elementary reactions from the original MCM and PRAM with the same reactants and kinetic constants into reactions with non-integer stoichiometric coefficients. After combining MCM and PRAM, the reference mechanism (MCM + PRAM in Table  1 ) contains 3 001 reactions, 1 227 species, and 738 condensables. • Jump rapidly-degraded species with a chemical lifetime of less than 1 s under the training dataset (see Section 2. 4. 2) or a kinetic rate constant greater than 10 6 s -1.
Evaluation dataset The evaluation datasets used for assessing the mechanism reductions comprise different groups of representative near-realistic atmospheric conditions. These datasets consist of the training and pre-testing datasets for different stages in the training process, as well as the testing dataset for the testing process. Fig. 4  illustrates the locations of the training, pre-testing, and testing datasets adopted for the monoterpene mechanism reduction. All the selected atmospheric conditions were extracted from the same database used for the sesquiterpene reductions reported in  Wang et al. (2022). This database includes CHIMERE simulation results performed over a one-year period (2015) over Europe. A monthly diurnal profile of hourly meteorological data was extracted from each condition (e.
g. , temperature, relative humidity), as well as hourly concentrations of oxidants, radicals, and other inorganic species. The evaluation datasets were selected according to the methodology described in  Wang et al. (2022). Specifically, different locations with varying API concentrations were selected to cover a range of concentrations of oxidants (OH, O 3 , and NO 3 ) and radicals (HO 2 , NO). The training dataset contains extreme atmospheric conditions representing different chemical regimes (e. g. , high NOx and low NOx conditions, high amount of O 3 , OH or NO 3 ) in order to guarantee that any changes in the mechanisms are tested over a wide range of environmental conditions. As shown in Table  2 , the training conditions are referred to as ''TC1'' to ''TC8'' and cover a broad range of environmental parameters, including variations in temperature from 270 K to 297 K and relative humidity (RH) from 9% to 100%.
The selection of training conditions is based on the reaction ratios of different oxidants (OH, O 3 , NO 3 radicals), NO 𝑥 , HO 2 , and RO 2 , as SOA formation depends on NO 𝑥 /HO 2 ratios and ozone concentrations  (Porter et al. , 2021). These ratios are calculated using the equations described in Table  S1 , with the kinetic constants extracted from the API degradation scheme in MCM. The training conditions cover high and low NOx regimes with NO ratios from 9% to 100% and ozone ratios from 17% to 67%. 2 ), pre-testing (100 conditions, navy dots), and testing datasets (9 818 conditions, white dots) for the monoterpene mechanism reduction. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ) a Columns from left to right list the condition identifier, latitude, longitude, time period, average temperature, average RH, average daily NO reaction ratio, and average daily O 3 reaction ratio.
b The calculation of reaction ratios is described in Table  S1. Once the mechanism has been reduced to a pre-defined accuracy, the training dataset is replaced by the pre-testing dataset, which is used to evaluate the general accuracy of the reduced mechanism under the most relevant conditions. The pre-testing dataset comprises 100 randomly selected conditions from the testing dataset (9 818 conditions). To ensure that the pre-testing dataset represents the general atmospheric conditions, the average reduction error using the pre-testing dataset is compared to the error using the testing dataset. The user can adjust the initially randomly selected pre-testing conditions so that the errors between the pre-testing and testing datasets are similar. This helps to ensure the accuracy and reliability of the reduced mechanism evaluation while minimizing any potential biases that may have arisen from the initial selection process.
The testing dataset initially is composed of 10 011 conditions, containing 4 011 conditions with high API concentrations (≥0. 2 μg/m -1 ) and 3 000 randomly selected conditions with medium (between 0. 1 μg/m -1 to 0. 2 μg/m -1 ) and low (between 0. 01 μg/m -1 to 0. 1 μg/m -1 ) API concentrations, respectively. From the initially selected testing dataset, 193 conditions with low SOA concentrations (with an hourly maximum SOA concentration lower than 1 μg/m -1 during the testing process) are excluded to avoid significant numerical uncertainty during evaluation. The final testing dataset consists of 9 818 conditions that are relevant to monoterpene SOA formation over Europe.
Training stages The reduction process is divided into four training stages, namely stages I, II, III, and IV. A summary of the key reduction parameters and options adopted in each stage can be found in Table  4. During each training stage, reduction cycles are performed, each consisting of multiple reduction steps that search for and evaluate all candidate reductions related to a targeted species. In each reduction step, only one candidate reduction is accepted if it meets all error tolerances and has the highest reduction score among all candidates. The predefined error tolerances adopted for reduction evaluation include thresholds for average and maximum reduction errors (𝜖) under the evaluation dataset, in comparison to the Ref. mechanism (i. e. , tolerances 𝜀 𝑟𝑒𝑓 𝑎𝑣𝑒 and 𝜀 𝑟𝑒𝑓 𝑚𝑎𝑥 ), and to the previously reduced mechanism (i.
e. , tolerances 𝜀 𝑝𝑟𝑒 𝑎𝑣𝑒 and 𝜀 𝑝𝑟𝑒 𝑚𝑎𝑥 ). The increase of the error 𝜖 𝑟𝑒𝑓 𝑎𝑣𝑒 induced by one reduction is also limited by a tolerance change threshold 𝛿𝜀. The reduction performance is evaluated after each reduction cycle by the reduction errors (𝜖 𝑝𝑟𝑒-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑎𝑣𝑒 and 𝜖 𝑝𝑟𝑒-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑚𝑎𝑥 ) using the pre-testing dataset. The user-defined error tolerances for the final reduced mechanism, denoted as 𝜀 𝑢𝑠𝑟 𝑎𝑣𝑒 for the average error and 𝜀 𝑢𝑠𝑟 𝑚𝑎𝑥 for the maximum error, are set to 3% and 30%, respectively. These two tolerances serve as criteria to constrain the errors 𝜖 𝑝𝑟𝑒-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑎𝑣𝑒 and 𝜖 𝑝𝑟𝑒-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑚𝑎𝑥 of the final reduced mechanisms at each stage. If no further reduction can be achieved with the applied reduction parameters and options of one stage, the training progresses to the next stage or terminates after stage IV.
The settings for each training stage are explained below. • Stage I: The reduction begins with the Ref. mechanism and undergoes evaluation against the training dataset with error tolerances ranging from 0. 05% to 3%. Once the algorithm no longer identifies any further reduction candidates within all sets of error tolerances in stage I or when the current errors (𝜖 𝑟𝑒𝑓 𝑎𝑣𝑒 or 𝜖 𝑟𝑒𝑓 𝑚𝑎𝑥 ) approach the final user-defined tolerances (𝜀 𝑢𝑠𝑟 𝑎𝑣𝑒 and 𝜀 𝑢𝑠𝑟 𝑚𝑎𝑥 ), the reduction process advances from stage I to stage II. • Stage II: Candidate reductions are tested against the pre-testing dataset. Given that the mechanism has already undergone a certain degree of reduction in stage I, the evaluation in stage II employs tolerances equal to 𝜀 𝑢𝑠𝑟 𝑎𝑣𝑒 and 𝜀 𝑢𝑠𝑟 𝑚𝑎𝑥 , allowing reductions with errors up to 3% on average and 30% at maximum.
To enhance the efficiency of the reduction process, the reduced mechanisms are solely compared to the reference mechanism against the pre-testing dataset in stage II and the subsequent stages. In other words, there are no further restrictions for errors 𝜖 𝑝𝑟𝑒 𝑎𝑣𝑒 and 𝜖 𝑝𝑟𝑒 𝑚𝑎𝑥 during these stages.
Results and discussion The monoterpene mechanism reduction was conducted using a 128-core computer and lasted for approximately four days. The final reduced monoterpene SOA mechanism, hereafter referred to as the ''MT-rdc'' mechanism, contains 197 reactions, 110 species, and 23 condensable species. As explained in Section 2. 4. 4, the training process for the monoterpene mechanism reduction can be divided into four consecutive training stages. The detailed training stages along with the resulting mechanism reductions are presented in Section 3. 1. The final obtained MT-rdc mechanism is described in Section 3. 2, and its complete lists of reactions and species are provided in the Supplementary Material. When evaluated against the testing dataset, MT-rdc introduces an average error of less than 3% over 9 818 conditions across Europe compared to the results simulated with Ref.
5%. As the reduction process continued, error tolerances progressively increased. At the end of stage I, the reduced SOA mechanism consisted of 449 reactions and 239 species, with an average error (𝜖 𝑟𝑒𝑓 𝑎𝑣𝑒 ) of 1. 6% under the pre-testing conditions. The reduction process in stage I resulted in significant reductions in the numbers of reactions, species, and condensables by 81%, 75%, and 88%, respectively, compared to the Ref. mechanism. After 1 559 approved reductions, the reduction proceeded to stage II, where the pre-testing dataset was used for reduction evaluation and the aerosol-oriented treatment was activated. Since the mechanism had been strongly reduced in stage I, the reductions achieved in stage II accounted for less than 8% of the total reduction. Compared to the stage I mechanism, the number of reactions and species were reduced by 28% and 33%, respectively.
Mechanism performance during testing The testing process is evaluated by comparing the differences in simulated total SOA concentrations between the reference mechanism (Ref. ) and the final reduced SOA mechanism (MT-rdc) under the testing dataset (consisting 9 878 testing conditions, explained in Section 2. 4. 2). The reduction error (calculation detailed in Section 2. 3. 4) over testing conditions is used to evaluate the efficiency of the reduction. In total, within 79 024 simulations, an average error of 2. 9% is obtained, which is consistent with the predefined error tolerance set for training. The errors over testing conditions for different initial SOA precursor conditions and starting times are summarized in Table  7. Overall, the testing errors for all initial conditions are acceptable, ranging from 2.
4%. This result indicates that MT-rdc exhibits higher accuracy for predicting SOA formation under spring and summer conditions in Europe than for fall and winter conditions in Europe. This discrepancy may be attributed to the pre-testing dataset having a higher proportion of conditions from May to September (83 out of 100 conditions) than from October to April (17 out of 100 conditions). Furthermore, significant errors are observed in the simulations conducted with the initial condition iniBPI compared to those performed with other initial conditions. This disparity is particularly prominent for the testing conditions during December, where the average error obtained with iniBPI is 11. 4%. However, it is worth noting that the SOA yields from BPI oxidation are significantly low compared to those from other monoterpene precursors, especially in December.
Mechanism sensitivity to environmental parameters The sensitivity of MT-rdc to several environmental parameters crucial for SOA formation is investigated. These parameters include temperature, relative humidity (RH), and SOA mass conditions. Average SOA yields under the pre-testing dataset are simulated for five days with two start times (0 h and 12 h), while varying environmental parameters. The default temperature and RH are held constant at 298 K and 50%, respectively, and the initial precursor condition follows the iniMT condition. The sensitivity to SOA mass is achieved by changing the concentration proportionally in the initial concentrations of precursors (the ratios between the different precursors are kept identical to iniMT). While changing one of the three parameters, the others are kept constant.
The original MCM + PRAM mechanism contains 3 001 reactions and 1 143 gas-phase species, with 738 species that can be considered condensables. After reduction, the monoterpene SOA mechanism (MT-rdc) is reduced to 197 reactions and 110 species, with the number of condensable species decreasing from 738 to 23. When evaluated against the testing dataset, MT-rdc introduced a low average error of less than 3% over Europe compared to the reference mechanism. Sensitivity tests demonstrated that MT-rdc behaves similarly to the original mechanism in response to changes in temperature, relative humidity, and SOA mass loading. By allowing a larger error tolerance of up to 20%, the SOA mechanism could be further reduced to a mechanism consisting of 40 reactions and 24 species, including only 5 condensables.
e The number of elementary-like reactions (the values within parentheses) is only noted at the start and end of elementary-like treatment (mechanisms of stages II and IV). f Errors 𝜖 𝑝𝑟𝑒-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑎𝑣𝑒 and 𝜖 𝑝𝑟𝑒-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑚𝑎𝑥 represent the average and maximum errors, respectively, obtained when evaluating the reduced mechanisms against the Ref. mechanism using the pre-testing dataset.
of species 239 160 130 110 No. of condensable 74 28 23 23 Error 𝜖 𝑝𝑟𝑒-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑎𝑣𝑒 f (%) 1. 6 2. 5 2. 6 3. 0 Error 𝜖 𝑝𝑟𝑒-𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑚𝑎𝑥 (%) 6. 2 26. 3 24. 9 28. 4 a For stage I, the values of 𝜀 𝑟𝑒𝑓 𝑚𝑎𝑥 / 𝜀 𝑝𝑟𝑒 𝑚𝑎𝑥 are applied to the reduction in the following order: 0. 5/ 0. 5, 1/ 1, 1. 5/ 1, 2/ 1, 2. 5/ 1, 3/ 1. For all training under pre-testing dataset, other tolerances are assigned accordingly: 𝜀 𝑟𝑒𝑓 𝑎𝑣𝑒 = 𝜀 𝑟𝑒𝑓 𝑚𝑎𝑥 /2, 𝜀 𝑝𝑟𝑒 𝑎𝑣𝑒 = 𝜀 𝑝𝑟𝑒 𝑚𝑎𝑥 /2, 𝛿𝜀 = 𝜀 𝑟𝑒𝑓 𝑚𝑎𝑥 /10. b Tolerance not used. c Record only those with approved reductions. d Mechanisms obtained after the respective training stages.
Data Analysis BL Lacertae was observed by IXPE with an exposure time of 287 ks on 2022 November 27-30 ). IXPE, launched on 2021 December 9, is a joint mission of NASA and the Italian Space Agency (Agenzia Spaziale Italiana, ASI). A description of the instrument is given by  Weisskopf et al. (2022). At the ∼30″ angular resolution of IXPE, BL Lacertae is a point-like source. Quasi simultaneously, BL Lacertae was observed in the hard and soft X-ray bands with NuSTAR (MJD 59911. 87-59912. 31), XMM-Newton (MJD 59910. 27-59910. 39), and Swift-XRT  (MJD 59910. 16, 59911. 89, 59912. 22, and 59913. 43) , in linear polarization in the optical BVRI bands with Calar Alto, the Nordic Optical Telescope (NOT), the 1. 8 m Perkins Telescope of Boston University, and the Sierra Nevada Observatory, and at millimeter wavelengths by the Institut de Radioastronomie Millimétrique 30 m Telescope (IRAM-30m) and SubMillimeter Array (SMA).
Additional low-frequency radio observations were performed with the Effelsberg 100 m telescope (4. 85-10. 45 GHz) and Korean very long baseline interferometry (VLBI) Network (KVN; 22-123 GHz). Appendices A and C detail the data reduction of these multiwavelength observations. The IXPE data were calibrated and reduced following standard procedures within the ixpeobssim pipeline 69   (Baldini et al. 2022). We begin by measuring the model-independent, constant polarization parameters for the full IXPE observation using ixpeobssimʼs PCUBE algorithm, finding 99% upper limits on the polarization fraction of 14. 3% (2-8 keV) and 19. 8% (2-4 keV). To characterize the contribution of the two emission components in the IXPE 2-8 keV band and identify a fiducial spectral model, we first attempt to fit the broadband X-ray spectrum averaged over the whole observation, including XMM-Newton 0.
The IAA-CSIC coauthors acknowledge financial support from the Spanish "Ministerio de Ciencia e Innovación" (MCIN/AEI/10. 13039/ 501100011033) through the Center of Excellence Severo Ochoa award for the Instituto de Astrofíisica de Andalucía-CSIC (CEX2021-001131-S), and through grants PID2019-107847RB-C44 and PID2022-139117NB-C44. Some of the data are based on observations collected at the Observatorio de Sierra Nevada, owned and operated by the Instituto de Astrofísica de Andalucía (IAA-CSIC). Further data are based on observations collected at the Centro Astronómico Hispano-Alemán (CAHA), operated jointly by Junta de Andalucía and Consejo Superior de Investigaciones Científicas (IAA-CSIC). The POLAMI observations were carried out at the IRAM 30 m Telescope. I. R. A. M. is supported by INSU/CNRS (France), MPG (Germany), and IGN (Spain).
The Submillimetre Array is a joint project between the Smithsonian Astrophysical Observatory and the Academia Sinica Institute of Astronomy and Astrophysics and is funded by the Smithsonian Institution and the Academia Sinica. Maunakea, the location of the SMA, is a culturally important site for the indigenous Hawaiian people; we are privileged to study the cosmos from its summit. The data in this study include observations made with the Nordic Optical Telescope, owned in collaboration by the University of Turku and Aarhus University, and operated jointly by Aarhus University, the University of Turku, and the University of Oslo, representing Denmark, Finland, and Norway, the University of Iceland, and Stockholm University at the Observatorio del Roque de los Muchachos, La Palma, Spain, of the Instituto de Astrofisica de Canarias.
The polarized intensity, position angle, and polarization percentage were derived from the Stokes I, Q, and U cross-scans. For frequencies <10 GHz, we find polarization degree Π R ∼ 5% with polarization angle ψ R between 148°and 188°. The radio spectrum obtained between 2. 6 and 43 GHz shows a smooth increase in flux toward higher frequencies, corresponding to a (hard) radio spectrum with spectral index of a = +0. 25 (S ∝ ν a ). We find a continuous rotation of the polarization angle between 4. 85 and 10. 45 GHz that suggests the presence of a Faraday screen with a rotation measure (RM) ∼ -230 rad m -2. The KVN observations were performed with two 21 m antennas (KVN Yonsei and Tamna) in single-dish mode. The    data reduction and polarization calibration was performed following  Kang et al.