dataesr/scientific-paragraphs-categorization huggingface dataset:
But, in detail, the surface HFD (given in mW/m 2 ) varies considerably between locations. Interpreting and understanding the global, regional and local thermal field requires in-depth knowledge of thermo-physical parameters and thermal processes. Unlike many other geophysical data, the collection of surface HFD is complicated by the need for temperature (T) data from boreholes, laboratory measurements of thermal conductivity (TC) as well as a detailed knowledge of local geological and geophysical data. Moreover, direct measurements of in-situ rock temperatures and thermo-physical rock properties are restricted to drillable depths of the sub-surface and perturbing thermal effects related to nearsurface phenomena or artificial thermal disturbances caused by drilling processes may overprint the equilibrium thermal field and affect HFD determinations.
Over the next few decades, heat-flow data were simply used, or combined with a variety of geophysical and geological methods in an attempt to reveal global and continental heat-flow distribution characteristics (e. g. ,  Chapman and Pollack, 1975; Sass and Lachenbruch, 1979; Pollack, 1982; Čermák and Rybach, 1991; Shapiro and Ritzwoller, 2004). Since 1963, the IHFC has been fostering the compilation of the Global Heat Flow Database (GHFD) to provide reliable heat-flow data. Reflecting the needs and technical capabilities at each respective time, the IHFC has released several data publications during its lifetime (e. g. ,  Lee, 1963; Lee and Uyeda, 1965; Lee and Clark, 1966; Simmons and Horai, 1968; Jessop et al. , 1976; Haenel et al. , 1988; Global Heat Flow Compilation Group, 2013). More recently and beyond the IHFC framework,  Hasterok (2019)  and  Lucazeau (2019)  published heat-flow data compilations based on the GHFD.
To meet the requirements for a thorough evaluation of the global heat flow database,  Jessop et al. (1976)  were the first to provide a data quality evaluation scheme based on their database structure and the compilation of the World Heat Flow Data Collection available at that time. In their contribution,  Jessop et al. proposed that individual users should make their own quality judgment based on the information available for each location, including: 1. the depth interval of measurement 2. the amount of data used to determine heat flow; and 3. any apparent variations in heat flow where multiple determinations were reported over short lateral distances  Jessop et al. (1976)  introduced one specific additional parameter termed "consistency". This parameter was particularly suited for shallow (marine) temperature-probe measurements, but it was also applied to other HFD data.
More recently,  Lucazeau (2019)  took advantage of this classification in his personal version of the GHFD allowing an improved application of the evaluation for both probe sensing and borehole data. He uses five classes depending on the variation of heat flow with depth: class A (very good, with a variation of <10%), class B (good, with a variation of <20%), class C (average, with a variation of <30%), class D (data not used in heat-flow maps, variation is >30%), and class Z (variation not specified). In principle, quality judgment by the user could also make use of further codes or metadata fields available in the published databases. However, this possibility has been severely hampered by the limited amount of metadata that has generally been provided, and hence available in the existing global database, for legacy heat flow determinations.
Moreover, users needed to make their individual assessment of the quality of data used, based on their particular application or research question. In 2020, the IHFC initiated an international project to review the GHFD (the Global Heat Flow Data Assessment project; http://assessment. ihfc-iugg. org) and to benefit from new developments in information technology. The goals of the reassessment were to: (1) provide an authenticated database containing information and associated metadata for each given HFD value, (2) establish a new evaluation scheme to define HFD quality, and (3) facilitate improvements to the database structure to fulfill the requirements of modern research data infrastructure including database interoperability. In 2021, these efforts resulted in the publication of a substantially renewed GHFD structure  (Fuchs et al.
, 2021a (Fuchs et al. , , 2021b)) , after which the attention focused on how the quality of individual HFD values could be assessed in an objective, comprehensible and well-documented way. In this paper, we present the new HFD quality evaluation concept. We first introduce the relevant terms for HFD determinations. Second, we outline why HFD data need a robust quality evaluation framework, and third, we propose a new HFD quality evaluation scheme. Finally, we demonstrate the first application of the new quality scheme using real heat-flow data (based on the IHFC data release of 2023  (Global Heat Flow Data Assessment Group et al. , 2023), covering 73,033 HFD values)  to test the suggested quality evaluation and to discuss benefits and limitations of the methodology. The suggested quality evaluation framework justifies an update of the IHFC database structure as previously defined in  Fuchs et al.
Heat-flow determination In the past, the IHFC distinguished two different types of heat-flow data: continental and marine. Continental data were those derived from boreholes and mines on the continents and marine data were mainly shallow temperature-probe measurements. Nevertheless, boreholes are drilled in the deep sea as well as probe measurements are made in lakes or rivers on the continents. Therefore, the classical distinction of these two data types as a basis for quality assessment is outdated. The acquisition method is considered a more appropriate attribute for categorizing a HFD determination. In continental boreholes, temperature gradient should ideally be determined below the deepest level at which temperature is significantly affected by the diffusion of surface climatic temperature perturbations.
g. , in fresh water wells) time has elapsed. The temperature gradient is an important input for the HFD quality evaluation, with quality related to the acquisition method and number of temperature data points. In addition, penetration depth, water depth and probe tilt are additional quality criteria that apply to temperature gradients obtained using probe sensing. In addition to direct techniques, subsurface temperatures may also be estimated using derivative methods, including the Curie point depth (e. g. ,  Tanaka et al. , 1999) , bottom-simulating reflector (BSR) depth (e. g. ,  Yamano et al. , 1982) , and lower-crustal high (electrically) conductive layer (e. g. ,  Majorowicz et al. , 1993). The Curie point depth is the depth at which rocks in a specific geographical area encounter the Curie temperature and lose their permanent magnetic properties; this is typically around 580 • C, the value for pure magnetite (e.
Beside interval method and Bullard plot, other methods are bootstrapping, a statistical hypothesis testing that involves the resampling of a single data set to create a multitude of simulated samples, or heat flow estimation from numerical modeling. HFD is determined in soft sediments (normally under water; marine onshore or offshore) by estimating undisturbed sediment temperatures using measurements from short sensors attached to drill bits, or customized shallow heat-flow probes with penetration depths from 50 cm to several meters. The word "shallow" refers here to the limited depth of penetration into the soft sediment and not to the water depth. Specialized instruments which penetrate into soft sediment have evolved to investigate steady-state conductive heat flow in the deep ocean. Historically, three types of design are usually categorized.
How to evaluate heat-flow data The evaluation of the global HFD data is aiming to classify the reliability of the acquired data, to provide a profound comparable database and to enable a deeper understanding of the thermal state of the Earth's lithosphere. To judge the reliability and quality of HFD related data, the "correct" characteristics for parameters affecting HFD determinations should be understood. Temperature information from the subsurface is influenced by all physical properties and processes relevant to the generation and detection of the respective thermal field. From this perspective, every derived HFD value reflects assumptions and interpretations about conditions at the HFD location. For example, the HFD may or may not have been determined over a depth interval under steady-state conditions and perturbed or not perturbed by drilling processes or paleoclimate effects.
Other authors, when determining HFD in a site nearby, might ignore heat refraction and not apply a correction to their data. Furthermore, the thermal conductivity needed for the HFD determination could be measured four times on the same sample, providing a mean thermal conductivity with a low standard deviation. But the sample may not represent the thermal conductivity over the depth interval of HFD determination. There might also be different HFD determinations at a single location derived and reported over different depths by different researchers. How should one judge which value represents the terrestrial surface heat flow more accurately? There is, therefore, great value in capturing and presenting information about the geological context and steps for data collection, processing and interpretation applied by different authors.
Heat-flow quality evaluation scheme Several prerequisites are necessary for the introduction of a new quality-evaluation scheme for the GHFD. The most important is that comprehensive relevant metadata for each HFD determination needs to be accessible and stored in the database. For this, a table-based database is no longer applicable. Relational databases allow storing data using a parent-child system, keeping the relevant unique data of a certain HFD site at one level and providing the detailed information for specific depth intervals and their thermal properties on a child level. This principle enables several HFD values to be stored for a site on the child level. A quality evaluation of the HFD values stored in the GHFD should be applied on the child level. Only the HFD value considered to best represent the 'true' undisturbed terrestrial surface heat flow is then linked to the parent level.
The parent-child system for the GHFD was already introduced by Fuchs et al. (2021a). Fig. 1  illustrates the concept and shows the most relevant parameters and processes necessary for HFD data evaluation. The parent level contains unique parameters for a single location (e. g. , HFD value and its uncertainty, geographical coordinates, site name as well as other metadata), including the terrestrial surface HFD value deemed the most accurate for the site. Each parent-level entry is associated with at least one child-level entry. The child-level entries contain detailed information about the depth intervals and associated temperature gradients and thermal conductivities of individual HFD determinations, including information about possible environmental perturbations and metadata like primary publication references (Appendix B).
In addition, if the same data are used in separate publications each would be recorded as an individual child element. This database structure allows the full documentation of all available data and context for one specific location without losing information, and provides a framework for presenting unambiguous HFD values for each environmental correction applied. The corresponding quality evaluation scheme we present in this paper provides a tool to identify the most accurate HFD value to be linked to the parent level and to provide the most consistent and quality-approved HFD database. Our proposed scheme for the evaluation of terrestrial surface heat flow data quality relies on three components: an uncertainty quantification (numerical), a methodological rating, and an evaluation of different perturbation effects (Fig.
2 ). The three components are finally combined into one overall quality score. The advantage of this threecomponent evaluation is to establish one scheme for all data despite different methodological quality, allowing a quick comparison for a first evaluation. As mentioned above, the quality of each HFD determination is evaluated at the child level of the GHFD, and the site-specific representative terrestrial HFD value is selected or derived from the relevant entries of the child level and elevated to the parent level. The rich metadata associated with each child level entry enables a user to easily search and select HFD values from the GHFD which meet certain quality criteria. In the following, we explain in more detail the three components of the new HFD quality evaluation scheme.
In borehole/mine settings, for instance, perturbed bottom-hole temperature measurements have larger penalties than unperturbed temperature logs, whereas in probe-sensing measurements, large penetration depths are afflicted with smaller penalties than small probe penetrations. Based on the final product of the temperature and conductivity score, the overall quality is quantified and used for the classification of the M-score (cf. Fig. 3 ). The larger the penalties and, thus, the lower the values of the T and TC score, the lower the product and thus the overall quality of the methodological approach. For both types of HFD determinations, the evaluation results in one of four quality classes, ranging from M1 (excellent) to M4 (poor) (Figs. 4  and 5 ). For datasets with insufficient or missing metadata (empty or unspecified fields), which do not allow to compute all the relevant parameters for T and TC scores, the largest penalty is assumed for the respective parameter.
Thermal conductivity. Evaluation criteria for the thermal conductivity quality score include 1) the location, 2) the source type and saturation condition, 3) the number of conductivity measurements and 4) the pressure and temperature conditions. Table  2  shows in detail the score reductions or enhancements based on the defined threshold values. The score starts at 1. 0 and varies from 0. 2 to 1. 2. The reduction for thermal conductivity data obtained from the literature or unknown location is 0. 2, for nearby or other locations 0. 1 and for in-situ measurements 0. The source type is divided into three blocks, offshore, onshore and estimated values. Offshore/shipboard measurements are distinguished from in-situ measurements, based on the source database entry and the relevant method, and recovered/ saturated conditions selected by the controlled vocabulary in the thermal conductivity saturation database field.
The latter selection applies also for onshore laboratory measurements, with the difference that samples are saturated, measured or calculated, by choosing the different relevant methods in the conductivity saturation database field (Table  2 ). Thermal conductivity estimated from the lithology, literature and water content/porosity or mineral composition are selected by the conductivity method field with penalties of -0. 1 and -0. 2, respectively. The number of conductivity points is similar to the points for gradient estimation. Pressure and temperature conditions are controlled by the chosen vocabulary of the relevant methods within the p-T condition database field. The score varies from -0. 2 for recorded/unrecorded ambient and unspecified conditions to +0. 1 for actual in-situ conditions.
Thermal conductivity. Thermal-conductivity scoring varies from 0. 1 to 1. 2. It starts at 1. 0 and is modified according to location, source type, number of measurement points, saturation, and p-T conditions (Table  3 ). Location depends on whether the interval depth is reported or the data originate from a nearby well. In the case that no interval depth is reported, the penalty is -0. 9 and the scoring stops, otherwise no penalty is applied. The relevant database fields constraining the decision are top and bottom of the respective heat-flow interval. Three cases of thermal conductivity allocations are considered. The penalty for values from the literature or unknown is -0. 2 and from nearby or other locations is -0. 1, while if thermal conductivity data are gathered from the actual HFD location no penalty is maintained.
The source type penalties vary from 0. 1 to -0. 2, for in-situ probe measurements/core-log integration and values assumed from general lithology and respective general textbook values, respectively. Scoring is dictated by the thermal conductivity source database field allowing controlled vocabulary entries (Appendix A). One of two penalties is applied for the number of conductivity points; a neutral rating for >15 measurements and a penalty of -0. 1 for 15 or fewer points or if the number of points is unknown. The thermal conductivity number in the database controls this decision. Saturation and p-T condition penalties are between 0 and -0. 2 and divided by saturated and dry measurements, controlled by the thermal conductivity saturation database entry and both separated for in-situ p-T (p or T individually) and ambient conditions (Table  3 ).
If saturated is chosen, the relevant method within the database field of thermal conductivity p-T conditions controls the penalty, either neutral for in-situ or -0. 1 for ambient conditions. Dry measurements are similar except that penalties for in-situ and ambient p-T conditions are -0. 1 and -0. 2, respectively. The combined scoring is presented in Fig. 3 , where the temperature is shown on the x-axis and conductivity on the y-axis.
Recognition of perturbations effects (p-flags) The third component of the quality assessment concerns possible site-specific perturbation effects. To evaluate the relevance of such effects at a particular site or HFD location and the validity of possible applied site-specific corrections would require much more site-specific information than is actually stored in the database. In principle, the authors of the HFD determination should have discussed perturbations and the applied corrections in their primary publication. In general, the effects of each possible perturbation to the HFD are well understood. But their site-specific relevance and correction approaches are matters of scientific discussion. Nevertheless, perturbation effects could overprint the terrestrial surface heat-flow and for an evaluation of the data it is helpful to know what kind of perturbation effects the respective authors had recognized.
Therefore, we propose a pragmatic and helpful indicator: the perturbation flags. These flags consist of seven consecutive letters representing seven different perturbation effects distinguished in the database (details are given in the following). The uppercase letter indicates that the effect is present and corrected, the lowercase letter indicates that the effect is present but not corrected. In the case that the effect is present but assumed to be insignificant an uppercase X is used and if the effect is not recognized or assumed to be not present a lowercase x is used (see Fig. 4 ). For data with insufficient information before the assessment took place (empty or unspecified) a minus sign (-) is used at the respective position in the final quality score.
IHFC database structure As outlined in Section 3, the new quality scheme requires an updated database structure, which is presented below. The updated structure    adopted the schemes from  Fuchs et al. (2021a) , from which the main elements are taken over (parent-child system, new metadata fields, field status system). For details on the change from the structure defined by  Jessop et al. (1976)  to the version from  Fuchs et al. (2021a)  we refer to the latter publication. A summary of the new scheme adopted for the quality rating is shown in Table  5 , with detailed definitions of the fields and entries including the applicable vocabulary documented in the Appendix A. The Appendix B provides an example of the application of  Besides (a) the introduction of new database fields, mostly motivated due to the requirements of the quality scheme, we combined the borehole/mine and probe-sensing scheme to (b) a unique scheme and introduced (c) a controlled vocabulary for specific entries, as these are crucial for the quality evaluation (for details see Appendix A).
Both, the vocabulary, which can be updated with new technology developments, and the combination of the former separated probe and borehole schema enables easier handling for end users. The field ID has been modified by entering a P for parent and a C for child in front of the field number to distinguish easily between the different levels and to avoid confusion. All data fields relevant to provide information on the HFD data origin and its geographic location are mandatory to ensure a reproducible database. All data fields necessary for applying the new quality scheme are mandatory (Table  5 ).
Parent level (P) On the parent level, 13 data entries are defined: nine of them are shared for probe-sensing and the borehole/mine schemes, and four additional entries are reserved for the borehole/mine scheme. Compared to  Fuchs et al. (2021a) , two new fields (P10 and P11), both related to the depths of the boreholes/mines, are introduced (Table  4 ). These additional entries provide further information on the general setting of the reported HFD determination. A former field named "13 heat-flow transfer mechanism" in  Fuchs et al. (2021a)  was removed from the database. By definition, the reported heat flow should represent conductive conditions. Advective environments are recorded in the respective flag field (e. g. , C18 in Table  4 ). The former fields "16 date of acquisition" and "33 bottom-water temperature" in  Fuchs et al.
Heat flow and site-specific metadata In comparison to  Fuchs et al. (2021a) , the probe-sensing relevant fields C21 (probe type) and C22 (probe length) were moved from the heat-flow to the site-specific metadata group, resulting in a total of six entries within the heat-flow child group. Because all fields of the heatflow group are relevant for the overall quality assessment, all are mandatory entries now. In the site-specific metadata group, 20 entries are given. 14 are shared with the borehole/mine and probe-sensing scheme with five additional relevant for probe-sensing. For the latter, a new recommended field (C20) allows information on the Expedition/ Platform/Ship whence the primary data were collected. This provides the end-user with a quick overview of additional geophysical and geological data available and is useful for further interpretations.
Discussion The reorganization and modification of the GHFD provides a transparent and authenticated database, enabling a comprehensible evaluation of the accessible heat-flow data from different user perspectives. Every heat-flow determination represents certain thermal conditions and methodological assumptions. Based on consistent and thorough documentation of the heat-flow determination our scheme allows an evaluation of the quality of heat-flow data according to three main criteria. In contrast to former approaches to data quality classification, which assigned one single subjective quality code to each heat-flow value (e. g. , "excellent" or "moderate"), the new approach allows an.