{"id":"5a940c87-a4f1-43fb-bb82-4a74d4a3fae3","arxiv_id":"2411.18748","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":6,"one_line_summary":"Photometric S-PLUS colors plus neural networks and random forests yield stellar parameters and abundance ratios for about five million stars, with a public catalog and quality flags, though the reliability of some element ratios is uncertain.","lead":"This paper uses machine learning on 12-band S-PLUS photometry to estimate temperatures, surface gravities, metallicities, and several element abundance ratios for about five million stars. It is a candidate resource for studying the Milky Way's stellar populations without needing a spectrum for every star.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 5-million-star reliability claim rests on extrapolating from a few thousand training stars; the metal-poor tail has only 9–121 stars per survey and the cluster validation pre-selects stars near the literature metallicity, so the claimed reliability is not demonstrated for most of the parameter…","rationale":"The reader's weakest-assumption diagnosis is correct and is the most load-bearing issue: the central claim of reliable estimates for ~5 million stars depends on extrapolating from a few thousand training stars with very sparse metal-poor coverage. I find the paper has real independent value: the catalog is public, the NN/RF comparison is systematic, the feature-importance analysis is useful, the flag system is a genuine attempt to communicate reliability, and the in-sample Teff/log g/[Fe/H] estimates are likely reasonable within the trained parameter space. However, the extrapolation concern is structural rather than cosmetic. The internal validation does not close it: Section 4.3 explicitly restricts cluster stars to those already close to the literature metallicity, and the paper admits the most metal-poor cluster tested (NGC 7099) is off by ~0.3 dex. The goodness-of-fit threshold of 60% is computed on test sets drawn from the same surveys and parameter ranges as the training data, so it says little about the ~25% of dwarf stars with features outside the training limits or about the metal-poor tail with 9-121 training examples. I therefore do not move the verdict; CONDITIONAL remains appropriate, with the condition that the authors either demonstrate extrapolation performance with a held-out metal-poor test or explicitly restrict the reliability claim to the FF=100, in-training-range subsample. Secondary concerns raised by the reader, such as absent per-star uncertainties and abundance ratios with no clear filter sensitivity (notably [Li/Fe]), are valid but subordinate to the distribution-shift problem.","tokens_in":31303,"tokens_out":7295,"duration_ms":71226,"concrete_test":"Retrain the NN and RF models on APOGEE and GALAH subsamples restricted to [Fe/H] > -1.5, and separately on stars whose input colors lie inside the original training range. Apply these restricted models to the held-out spectroscopic stars with [Fe/H] < -1.5 and to stars with colors outside the training range, then compare predictions against the spectroscopic labels in bins of [Fe/H]. If the median bias exceeds ~0.15-0.2 dex or the scatter in the [Fe/H] < -2 bins is more than twice the in-sample scatter, the 'reliable for ~5 million stars' claim fails for the extrapolated population. Additionally, rerun the Section 4.3 cluster comparison without the 0.2*(1+|[Fe/H]_lit|) pre-selection filter to quantify how much of the reported agreement depends on that circular selection.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract's claim of 'reliable estimates' for ~5 million stars requires that the small spectroscopic training samples generalize across the full S-PLUS sample. Section 2 gives training sizes of ~2,877 (APOGEE), ~5,916 (GALAH), and ~573 (LAMOST) after cross-matching. Table 2 shows the metal-poor tail is tiny: 66, 121, and 9 stars for [Fe/H] in (-3,-2), and only 14, 34, and 4 stars below -3. Section 4.1 then applies the models to ~5 million stars, and when input features fall outside the training range they are clamped to the training minimum or maximum. The paper's own text warns this 'can introduce biases', and Table 3 shows roughly 25% of dwarf stars do not have all features inside the training-set limits. The goodness-of-fit >60% is a test-set statistic from the training distribution; it does not quantify error under this distribution shift. The principal external validation, Section 4.3, selects cluster members only if their photometric [Fe/H] is within 0.2*(1+|[Fe/H]_lit|) of the literature value before computing agreement, an internal admission of selection bias. The paper also reports NGC 7099 ([Fe/H]~-2.29) is overestimated by ~0.3 dex. Thus the central claim is not supported for the extrapolated metal-poor and feature-out-of-range portions of the 5-million-star catalog, and the flags, while helpful, are not part of the headline claim.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper presents photometric estimates of stellar atmospheric parameters (Teff, log g, [Fe/H]) and elemental abundance ratios for ~5 million sources in S-PLUS DR4, using cost-sensitive neural networks and random forests trained on spectroscopic labels from LAMOST, APOGEE, and GALAH. The input representation is the 66 S-PLUS colors, and three strategies (colors alone; colors plus Teff; colors plus Teff and log g) are compared. Parameters with holdout goodness-of-fit above 50% are retained, feature-importance analyses are presented, and a flagged catalog is released at CDS. Validation is attempted through star-cluster memberships, the TESS Input Catalog, and a comparison with J-PLUS predictions and GALAH spectroscopy for 186 common dwarfs and 8 giants. The central claim, stated in the abstract and conclusions, is that 'reliable estimates' of Teff, log g, [Fe/H], [alpha/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] are obtained for roughly 5 million stars with goodness-of-fit above 60%, with additional, less accurate estimates for [Cu/Fe], [O/Fe], and [Si/Fe].","tokens_in":31586,"tokens_out":15343,"duration_ms":127116,"significance":"If the headline claim were fully supported, the catalog would be a valuable community resource, extending abundance estimation to a sample an order of magnitude larger than current spectroscopic surveys and providing a framework reusable for S-PLUS DR5 and the Ultra-Short Survey. The paper has real strengths: the use of three independent training surveys, the deliberate inclusion of abundances without corresponding S-PLUS features as a spurious-correlation probe, a transparent flag system (FF, Flag 00-02, FlagTIC), and a public catalog. The physical coherence checks, such as the [Mg/Fe]-[Fe/H] bimodality and the Galactic metallicity gradients, are encouraging. However, the evidence as presented does not support the strongest claim. The test-set R^2 statistics are computed within the training distribution; the metal-poor tail of the training sets is extremely sparse (Table 2); the [Li/Fe] claim conflicts with the paper's own caution about abundances lacking S-PLUS features; and the principal external validations are weakened by selection on the predicted quantity (Section 4.3) and by overlap or methodological kinship of the comparison catalogs (Sections 4.4-4.5).","major_comments":[{"comment":"The claim in the abstract and conclusions that 'reliable estimates' are obtained for approximately 5 million stars is not demonstrated for the parts of the catalog outside the training distribution. The models are trained on a few thousand to a few tens of thousands of cross-matched sources per survey (Section 2 gives 2,877, 5,916, and 573 sources for APOGEE, GALAH, and LAMOST after cross-matching, while the per-parameter counts in Table 1 are several times larger), and Table 2 shows the metal-poor tail is extremely sparse: 66, 121, and 9 stars for -3 < [Fe/H] < -2, and 14, 34, and 4 stars below -3. Section 4.1 then applies the models to about 5 million sources, clamping features outside the training range to the minimum or maximum and stating that this 'can introduce biases'; Table 3 shows that a non-negligible fraction of dwarf stars have at least one feature outside the training limits (for example, about 32% of the LAMOST-based dwarf Teff sample falls below the 100% column). Because the reported goodness-of-fit above 60% is a test-set statistic drawn from the training distribution, it does not quantify error under this distribution shift, and no out-of-distribution calibration is provided. The reliability statement should be conditioned on the FF flag and on the parameter ranges, or an explicit extrapolation test (for example, against a metal-poor spectroscopic sample) should be supplied.","section":"Abstract; Sections 2 and 4.1; Tables 2-3"},{"comment":"The abstract and Section 5 list [Li/Fe] among the reliable estimates with goodness-of-fit above 60%, but no S-PLUS filter is centered on a lithium line (Section 1 lists the narrowband features as [O II], Ca H+K, H-delta, CH G-band, Mgb triplet, H-alpha, and Ca triplet), and Section 2 explicitly warns: 'It is crucial to exercise caution when interpreting elemental abundances that lack corresponding features in S-PLUS filters.' A high test-set R^2 for [Li/Fe] is expected even if the model merely reproduces the known correlations of lithium with Teff, log g, and [Fe/H], which the S-PLUS colors do constrain; the R^2-based Flag 00 therefore does not establish that the lithium abundance itself is recovered. To support the claim, the authors should compare the photometric [Li/Fe] predictions against a null model that predicts [Li/Fe] from Teff, log g, and [Fe/H] alone (showing that the colors add predictive power beyond these parameters), or validate against lithium measurements from a spectroscopic sample, or explicitly reclassify [Li/Fe] as a lower-reliability, correlation-based estimate in the abstract, conclusions, and flag definitions.","section":"Abstract; Section 2; Section 4 flag definitions; Table 1"},{"comment":"The star-cluster validation of [Fe/H] selects stars using the quantity being validated: 'we focus only on stars whose metallicities are close to those reported in the literature, applying an error tolerance of 0.2 x (1 + |[Fe/H]_Literature|).' Because the photometric metallicity is part of this preselection, the agreement shown in Figure 10 is inflated and the procedure is partially circular; the authors acknowledge that the criterion 'introduced some bias into the results.' Furthermore, the most metal-poor cluster tested, NGC 7099 with [Fe/H] approximately -2.29, is overestimated by about 0.3 dex, which is precisely the regime where Table 2 shows the training data to be thinnest. The cluster validation should be recomputed without the metallicity preselection (relying on membership probability and photometric quality cuts alone), and the metal-poor offset should be incorporated into the reliability statements.","section":"Section 4.3; Figure 10"},{"comment":"The two remaining external checks do not provide fully independent confirmation of accuracy. Section 4.4 compares with the TESS Input Catalog and concedes that some of its entries 'may overlap with our training set,' and the [M/H]-[Fe/H] relation of Equation (2) is then calibrated on that same comparison sample (Table 4), making the exercise a consistency check rather than an accuracy test. Section 4.5 compares S-PLUS predictions with J-PLUS predictions produced by the same cost-sensitive neural-network methodology (Yang et al. 2022) for only 186 dwarfs and 8 giants; agreement between two applications of the same method is not an independent test of the abundance scale. The authors should either add a genuinely independent validation set (for example, high-resolution abundances for stars excluded from training) or soften the abstract claim that star clusters, TESS, and J-PLUS data 'confirmed the robustness of our methodology.'","section":"Sections 4.4-4.5"}],"minor_comments":[{"comment":"The text states that the cross-match yielded about 2,877, 5,916, and 573 sources for APOGEE, GALAH, and LAMOST, while Table 1 reports per-parameter counts that are several times larger (for example, 8,885 APOGEE dwarfs and 5,500 APOGEE giants for Teff); the relationship between these numbers should be clarified, as it determines the effective training-set sizes quoted by future users.","section":"Section 2 vs Table 1"},{"comment":"The sentence 'the lack of correlation between Teff and metallicity is not expected' presumably should read 'is expected'; as written, it states the opposite of the argument developed in the following sentences.","section":"Section 4.2"},{"comment":"The sentence 'with sigma values smaller than -0.5 dex' appears to contain a sign error; the context indicates the intended statement is sigma values smaller than 0.5 dex.","section":"Section 4.3"},{"comment":"The phrase 'S-PLUS surplases JPLUS' contains a typo and should read 'surpasses.'","section":"Section 4.5"},{"comment":"The caption contains the typo 'trainning' and should read 'training.'","section":"Figure 3 caption"},{"comment":"The statement that 'about 75% of dwarf stars fall within the constraints of the training sets' does not match the 100% column of Table 3 for any single row, where the fraction ranges from roughly 68% (LAMOST) to roughly 89% (APOGEE and GALAH Teff); the aggregation used for this number should be stated.","section":"Section 4.1; Table 3"},{"comment":"Appendices A through G are referenced as 'see link,' so the goodness-of-fit tables, feature-importance plots, catalog description, and cluster comparison tables are not available in the manuscript itself; these materials should be included or clearly referenced to a stable location in the revised version.","section":"Appendices A-G"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is within A&A's scope and the catalog will be a community resource. My principal concern for the editor is the current tension between the unqualified headline reliability claim and the paper's own flag-and-caveat infrastructure; the revision should make the reliability statements match the flags and training coverage. I also recommend that the authors resolve the inconsistency between the training-set sizes quoted in the text and those in Table 1 before publication, since these numbers will be quoted by other papers. The requested changes are local to the claims and validation framing rather than to the methodology itself, so I view major revision as appropriate rather than rejection."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper delivers a useful public catalog: photometric Teff, log g, [Fe/H], and several abundance ratios for ~5 million S-PLUS DR4 stars, trained on APOGEE, GALAH, and LAMOST with a cost-sensitive NN and RF comparison. The core Teff/log g/[Fe/H] estimates look defensible, especially with the feature-flag system (FF) that tells users when inputs fall outside the training range. The spurious-correlation test, the train/test split, and the multi-survey training are all sensible and well executed. This is a real resource for Milky Way population studies and target selection.\n\nThe soft spots are real but localized. The main one is the abstract's claim of reliable [Li/Fe], [O/Fe], [Cu/Fe], and [Si/Fe]. S-PLUS filters have no lithium feature, and the paper's own cautionary note about abundances lacking filter coverage is undercut by listing [Li/Fe] among the 'reliable' estimates. The high R² almost certainly traces Teff/log g correlations; the spurious-correlation control is a good idea but it does not rescue this particular claim. The cluster validation is weakened by pre-selecting members within 0.2*(1+|[Fe/H]_lit|) of the literature metallicity, so the reported agreement is partly built in. The paper admits this but does not quantify the bias. There are also no per-star uncertainties, which limits the catalog's use for precision work, and the metal-poor tail is thin (9–121 stars per survey below [Fe/H]=-2) with ~25% of dwarfs having some features clamped to training limits. The paper flags these issues, which is honest, but the abstract oversells.\n\nThe J-PLUS and TIC comparisons are weaker than they first appear: J-PLUS uses the same NN method, TIC overlaps with training samples, and the overlap is tiny (186 dwarfs, 8 giants). Minor point.\n\nWho is this for? Astronomers who want a large, flags-aware photometric parameter catalog for S-PLUS DR4 and are willing to apply their own cuts. It deserves a serious referee. The referee should push for downgrading the abundance language (especially [Li/Fe]), adding per-star uncertainties or a clear statement of why they are omitted, and moving the validation-bias caveat into the abstract. Not a desk reject.","headline":"A genuinely useful S-PLUS stellar parameter catalog with credible Teff/logg/[Fe/H] estimates, but the headline claims of 'reliable' abundances for Li, O, Cu, and Si overstate what the filters and validation actually support.","tokens_in":32411,"tokens_out":2050,"would_cite":true,"duration_ms":21260,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A photometric survey can stand in for spectroscopy to measure the chemistry of about five million stars.","keywords":["S-PLUS survey","photometric stellar parameters","chemical abundances","machine learning","cost-sensitive neural network","random forest","Galactic stellar populations","narrowband photometry"],"falsifier":"Compare the catalog's $[\\mathrm{Fe/H}]$ values for a few hundred stars with spectroscopic $[\\mathrm{Fe/H}] < -2$ that were not used in training; if the photometric values show systematic offsets comparable to the roughly 0.3 dex excess already seen for M30, the extrapolation claim would fail.","tokens_in":31032,"feed_emoji":"🌟","tokens_out":7120,"duration_ms":56817,"temperature":0.7,"pith_summary":"This paper claims that the 66 colors formed from the 12 S-PLUS photometric bands carry enough information to estimate effective temperature, surface gravity, iron abundance, and several elemental abundance ratios for roughly five million Milky Way stars, without taking a single spectrum. The models, cost-sensitive neural networks and random forests trained on overlapping APOGEE, GALAH, and LAMOST stars, recover $T_{\\rm eff}$, $\\log g$, $[\\mathrm{Fe/H}]$, $[\\alpha/\\mathrm{Fe}]$, $[\\mathrm{Al/Fe}]$, $[\\mathrm{C/Fe}]$, $[\\mathrm{Li/Fe}]$, and $[\\mathrm{Mg/Fe}]$ with goodness-of-fit above 60%, once the 66 colors and estimated $T_{\\rm eff}$ and $\\log g$ are used as inputs. If the estimates hold, the catalog turns narrowband photometry into a spectroscopic-scale chemical map of the disk, enabling population and chemo-dynamical studies that currently require high-resolution spectroscopy. The authors validate the results against star-cluster metallicities, TESS input catalog data, and J-PLUS predictions, and flag stars whose features fall outside the training ranges.","feed_headline":"Photometry alone yields stellar chemistry for 5 million stars","feed_subtitle":"Trained on three spectroscopic surveys, S-PLUS colors recover temperatures, gravities, and abundance ratios without spectra.","key_machinery":"The load-bearing object is a cost-sensitive neural network: a six-layer feed-forward network with 1664 neurons that takes the 66 S-PLUS colors as input and weights rare training cases more heavily, so that under-represented parameter values are not ignored. In the preferred configuration, the network first estimates $T_{\\rm eff}$ and $\\log g$ from the 66 colors, then uses those estimates as additional input columns (68 features total) to predict each abundance ratio; a random forest trained on the same inputs serves as a cross-check, and an r2-based goodness-of-fit decides which parameters are kept. A feature-flag system records what fraction of a star's input features lie inside the training-set limits, since out-of-range colors are clipped to the training minimum or maximum.","core_discovery":"On its own terms, the discovery is that the narrowband S-PLUS system is not just a stellar classifier but a chemical-abundance probe: using the twelve observed magnitudes to build all pairwise colors, and training a cost-sensitive neural network on spectroscopic labels from APOGEE, GALAH, and LAMOST, the paper estimates stellar atmospheric parameters and abundance ratios for about 140,000 giants and 4.9 million dwarfs. The neural network consistently beats the random forest, and feeding $T_{\\rm eff}$ and $\\log g$ back into the network as extra features improves accuracy by about 3%, with the largest gains for $[\\mathrm{Fe/H}]$ and $[\\mathrm{Mg/Fe}]$. Only parameters with goodness-of-fit above 50% across all approaches are kept, and the most reliable ones, including $T_{\\rm eff}$, $\\log g$, $[\\mathrm{Fe/H}]$, $[\\alpha/\\mathrm{Fe}]$, $[\\mathrm{Al/Fe}]$, $[\\mathrm{C/Fe}]$, $[\\mathrm{Li/Fe}]$, and $[\\mathrm{Mg/Fe}]$, exceed 60%; $[\\mathrm{Cu/Fe}]$, $[\\mathrm{O/Fe}]$, and $[\\mathrm{Si/Fe}]$ are released with cautionary flags. The paper further shows that the estimates reproduce known Milky Way trends, such as the radial iron gradient and the bimodal $[\\mathrm{Mg/Fe}]$-$[\\mathrm{Fe/H}]$ distribution, and can be used to select star-cluster members by metallicity.","pith_inferences":["A reader should treat the metal-poor tail ($[\\mathrm{Fe/H}]$ below about -2) as the untested edge: the training sets contain only tens of stars there, and the paper's own M30 comparison shows a roughly 0.3 dex overestimate, so catalog values in that regime are best used as candidates, not measurements.","Because the narrowband filters J0378, J0395, J0410, and J0430 plus the u-band dominate feature importance, the method suggests that even a few well-chosen medium bands can carry most of the chemical-abundance information; a future survey could optimize filter placement around those features.","Combining this catalog with Gaia astrometry should make it possible to separate thin-disk and thick-disk populations on a sample of millions of stars, which the paper notes as a forthcoming application rather than a completed one."],"forward_implications":["The released catalog gives roughly five million stars with $T_{\\rm eff}$, $\\log g$, $[\\mathrm{Fe/H}]$, $[\\alpha/\\mathrm{Fe}]$, $[\\mathrm{Al/Fe}]$, $[\\mathrm{C/Fe}]$, $[\\mathrm{Li/Fe}]$, and $[\\mathrm{Mg/Fe}]$, a sample size competitive with large spectroscopic surveys but obtained from photometry.","The estimated metallicities can identify likely star-cluster members and reject interlopers, as demonstrated on globular and open clusters.","The S-PLUS-based estimates reproduce the Milky Way's radial metallicity gradient and the bimodal $[\\mathrm{Mg/Fe}]$-$[\\mathrm{Fe/H}]$ distribution, so the catalog can support chemo-dynamical studies of the disk.","The same trained models are ready to apply to S-PLUS IDR5 and the Ultra-Short Survey, roughly doubling the data volume and extending the sky coverage without new spectroscopy."],"supporting_citations":[{"why":"Supplies the cost-sensitive neural network architecture and the J-PLUS precedent that this work adapts to S-PLUS.","marker":"Yang et al. (2022)"},{"why":"Defines the S-PLUS survey and its 12-filter system from which the 66 colors are built.","marker":"Mendes de Oliveira et al. (2019)"},{"why":"Provides S-PLUS DR4, the photometric catalog that supplies the input magnitudes.","marker":"Herpich et al. (2024)"},{"why":"Provides Gaia DR3 $T_{\\rm eff}$ and $\\log g$ used to separate dwarfs from giants in the training and application samples.","marker":"Gaia Collaboration et al. (2021)"},{"why":"Defines the PStotal aperture photometry correction used for the S-PLUS magnitudes.","marker":"Almeida-Fernandes et al. (2022)"},{"why":"Provides the random forest algorithm used as the second, cross-checking estimator.","marker":"Breiman (2001)"},{"why":"Supplies the TIC validation sample and the $[\\mathrm{M/H}]$-$[\\mathrm{Fe/H}]$ relation used to flag inconsistent metallicities.","marker":"Stassun et al. (2019)"},{"why":"Supplies globular cluster membership probabilities used to validate the estimated metallicities.","marker":"Vasiliev & Baumgardt (2021)"}],"fun_headline_variants":["S-PLUS photometry maps chemistry for 5 million stars","Machine learning turns colors into stellar abundances","Narrowband survey delivers stellar parameters for 5M stars","No spectra needed: photometry reveals stellar chemistry","5 million stars get chemical abundances from S-PLUS"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The models are trained on a few thousand stars per survey and almost no stars below $[\\mathrm{Fe/H}] = -2$, yet they are applied to about five million S-PLUS stars whose out-of-range colors are clamped to the training limits; the assumption is that this extrapolation still produces meaningful values.","fun_headline_variants_meta":{"raw":{"variants":["S-PLUS photometry maps chemistry for 5 million stars","Machine learning turns colors into stellar abundances","Narrowband survey delivers stellar parameters for 5M stars","No spectra needed: photometry reveals stellar chemistry","5 million stars get chemical abundances from S-PLUS"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000212,"raw_usage":{"total_tokens":1564,"prompt_tokens":1237,"completion_tokens":327,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":853,"completion_tokens_details":{"reasoning_tokens":249}},"tokens_in":853,"tokens_out":327,"duration_ms":92913,"temperature":1.0,"reasoning_tokens":249,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T10:55:10.636846+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Compare the catalog's $[\\mathrm{Fe/H}]$ values for a few hundred stars with spectroscopic $[\\mathrm{Fe/H}] < -2$ that were not used in training; if the photometric values show systematic offsets comparable to the roughly 0.3 dex excess already seen for M30, the extrapolation claim would fail.","supporting_citations":[{"cited_title":"2022, A&A, 659, A181","cited_arxiv_id":null,"evidence_quote":"Supplies the cost-sensitive neural network architecture and the J-PLUS precedent that this work adapts to S-PLUS."},{"cited_title":"R., Almeida-Fernandes, F., Oliveira Schwarz, G","cited_arxiv_id":null,"evidence_quote":"Provides S-PLUS DR4, the photometric catalog that supplies the input magnitudes."}],"review_version":1}