dataesr/scientific-paragraphs-categorization huggingface dataset:
Limnological Behavior and Meteorological Data The water quality and trophic level of a lake primarily depend on nutrient inputs (Nitrogen and Phosphorus) from the watershed. This is why they are selected in conjunction with the transparency, the temperature, and the study variable chlorophyll a. All these parameters influence the spatio-temporal distribution of algae. Figure  7  shows the behavior of limnological variables associated with algal blooms. The boxplot shows the distribution of the numerical dataset of the variables indicated by five key statistics: minimum value, first quartile (Q1), median (Q2), third quartile (Q3), and maximum value (Q4). Chlorophyll-a values vary widely depending on location, season, and environmen conditions (see Table  2 ). Chl-a in Lake Llanquihue ranged from a minimum of Q1 = 0 µg/L to a maximum of Q4 = 2.
Conclusions Combined remote sensing and machine learning techniques have proven to be valuable tools for the estimation of environmental proxies, such as chlorophyll-a. Coefficient of determination values ranging from 0. 64 to 0. 93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performs well across most stations, with lower values for MSE (<0. 260 (µg/L) 2 ), RMSE (<0. 510 ug/L), MaxError (<0. 730 µg/L), and MAE (<0. 442 µg/L). This parameter has been widely used in different aquatic ecosystems as an indicator of algal biomass and water quality. In this study, a series of in-situ data from 1989 to 2021 recorded at eight monitoring stations spatially distributed in Llanquihue Lake was used to study the behavior of limnological variables at different points in the lake.
A (Measurement Data): In the first case, we included the real variables measured in the monitoring campaigns for the four seasons of the year and in the eight stations of the lake. Case B (Measurement and Meteorological Data): In addition to the actual variables, we included meteorological data as conditioning variables that can influence the autochthonous processes of the lake. Case C (Measurement, Meteorological Data and Satellite Data): In this case, we include bands and indices from the L-8 satellite image processing.
Statistical Analysis We adopted several statistical approaches to assess the character of the mPWP and to investigate how the climate system evolved across the iNHG. Previous analysis of the 5-0 Ma LR04 δ 18 O benthic stack using a Bayesian Change Point algorithm identified a change point at 2. 73 Ma (±0. 1 Ma), closely aligning with iNHG, which was marked by an increase in the obliquity signal  (Ruggieri, 2013). To test whether regime boundaries also existed in the PlioVAR data sets (e. g. a shift in the mean or variance of the climate data), we applied the same Bayesian Change Point algorithm to the 53 individual proxy datasets and the three sea-level records which had high-resolution data for the whole 3. 3-2. 4 Ma interval (Table  1 ). This algorithm calculates the probability density of the input time series, and uses a regression model and the combined application of forward recursion and Bayes rule to compute change point locations and their probability.
Although warming and retreat of the West Antarctic ice sheet recorded in the Amundsen Sea starts from ∼4. 2 Ma and extends through the mPWP, more extensive West Antarctic glaciation from MIS M2 ∼3. 3 Ma is indicated by ice-proximal cores in the Ross Sea  (McKay et al. , 2012; Riesselman & Dunbar, 2013). The relatively early onset of Southern Hemisphere δ 18 O benthic change points through the mPWP may reflect ice sheet impacts on Southern Ocean circulation. However, the complexity of the Antarctic signals, showing both advance and retreat of different parts of the ice sheet through time, may have influenced the observed asynchroneity of the δ 18 O benthic change points. We return to these complexities in Section 4. 5, where we assess the evidence for changing orbital-scale variability in our PlioVAR data sets and evidence for ice-volume change both during and after the mPWP.
Q3: What were the characteristics of the iNHG? We have shown that the iNHG was not a globally synchronous event. We confirmed previous suggestions that long-term changes in climate and ice sheets began before and during the mPWP (a longer-term "oNHG"). Asynchronous transitions in δ 18 O benthic records from individual sites confirm the importance of identifying and assessing the relative importance of local influences over δ 18 O benthic (e. g. temperature, salinity) which are in turn linked to high-latitude surface ocean properties, alongside the more integrated signature of global ice volume. Our review of existing data confirms that the iNHG was complex in terms of ice-sheet advances/retreats, in both timing and magnitude. A global expression of ice volume change is not indicative of how dynamic the individual ice masses were across the complete oNHG and iNHG.
Our analysis has demonstrated the complex, non-uniform and globally asynchronous nature of climate changes associated with the oNHG and iNHG. The evidence available today indicates that shifting ocean gateways and ocean circulation changes may have pre-conditioned the later evolution of ice sheets with falling pCO 2 , but there remains complexity in confirming the mechanism(s) or sequence of events which influenced the timing of ice sheet expansion and its expression in marine records. We recommend that future work targets multi-proxy data sets with high time-resolution, which can integrate our understanding of the environmental processes controlling and responding to the changes in SST and ice volume outlined here, which will continue to be critical to develop our understanding of the causes and impacts of past warm climates and the transitions between different climate regimes.
Data Availability Statement All original data-sets are available via the NOAA and PANGAEA online repositories and can be accessed through the citations and database DOIs given in Table  1 , with full references provided below. The data used here, including any which were changed from the original publication due to differences in age control or proxy calibration, and the results of the analyses we performed, are available at https://doi. org/10. 1594/PANGAEA. 956158 (under moratorium). Data that we retrieved which was not on the NOAA or PANGAEA databases is also available at  McClymont et al. (2023) , https://doi. org/10. 1594/PANGAEA. 956158. The data we have used can also be accessed via our interactive web portal (https://pliovar. github. io/iNHG. html), including an option to download all data as a single file.
org/10. 1594/ PANGAEA. 789866, https://doi. org/10. 1029/2008PA001645 https://doi. org/10. 1594/PANGAEA. 804671, https://doi. org/10. 1594/ PANGAEA. 789867 https://doi. org/10. 1594/PANGAEA. 874748 http://lorraine-lisiecki. com/stack. html https://doi. org/10. 1594/PANGAEA. 863960 https://doi. org/10. 1594/PANGAEA. 863938 https://doi. org/10. 5194/cp-14-339-2018 https://doi. org/10. 25921/52j8-rt05 https://doi. org/10. 1594/PANGAEA. 58717 https://doi. org/10. 1594/PANGAEA. 729403 https://doi. org/10. 1594/PANGAEA. 316972 Original publication Clotten et al. (2018) Lawrence et al. (2009) Lisiecki and Raymo (2005) Naafs et al. (2010) Hennissen et al. (2014), De Schepper et al. (2013) Bolton et al. (2010) Hennissen et al. (2014), De Schepper et al. (2013) Groeneveld et al. (2014), De Schepper et al.
(2009)1594/PANGAEA. 701400(G. ruber)Karaset al. (2009)https://doi. org/10. 25921/b2z7-3f44 37 Herbert et al. (2010) https://doi. org/10. 25921/we6n-qe21 37 McClymont et al. (2016) https://doi. org/10. 25921/13rs-tp69 37 Caballero-Gill et al. (2019) https://doi. org/10. 1594/PANGAEA. 898167 Caballero-Gill et al. (2019) https://doi. org/10. 1594/PANGAEA. 898167 37 Martinez-Garcia et al. (2010) https://doi. org/10. 1594/PANGAEA. 771708 https://doi. org/10. 1594/PANGAEA. 956158 Note.
Resultant computations for the multiplicity set The algorithm ms_resultant exploits the following fact: For a bivariate polynomial system, the resultant with respect to one of the variables is a univariate polynomial (in the other variable) and the multiplicities of its roots accumulate the multiplicities of the roots of the system in the fiber. The pseudo-code of the algorithm appears in Alg. 4 and its proof of correctness presents the details of the various steps. Proposition 6. 1. ms_resultant, Alg. 4, correctly computes the A-multiplicity set AF K (Γ) that corresponds to a pertinent edge Γ ≺ A. The result is a (possibly empty) union ρ C ρ of finitelymany curves C ρ that are the zero loci of polynomials in K[y 1 , y 2 ], where K ∈ {R, C} and ρ runs over all elements of Sol K (Γ). Proof.
Acknowledgments BEH is supported by the  DFG Walter Benjamin Program  grant  EL 1092/1-1. Part of this work was supported by the  Austrian Science Fund (FWF) :  P33003. This work was initiated while BEH was at the  Institute of Mathematics at the Polish Academy of Sciences in Warsaw. Thanks to  Zbigniew Jelonek  for presenting the problem and to  Piotr Migus  for fruitful discussions. ET is supported by the  ANR   JCJC GALOP  ( ANR-17-CE40-0009 ), the  PGMO grant ALMA , and the  PHC GRAPE.
This chapter presents the concept of Digital Language Equality (DLE) that was at the heart of the European Language Equality (ELE) initiative, and describes the DLE Metric, which includes technological factors (TFs) and contextual factors (CFs): the former concern the availability of Language Resources and Technologies (LRTs) for the languages of Europe, based on the data included in the European Language Grid (ELG) catalogue, while the latter reflect the broader socio-economic contexts and ecosystems of the languages, as these determine the potential for LRT development. The chapter discusses related work, presents the DLE definition and describes how it was implemented through the DLE Metric, explaining how the TFs and CFs were quantified. The resulting scores of the DLE Metric for Europe's languages can be visualised and compared through the interactive DLE dashboard, to monitor the progress towards DLE in Europe.
"Athena", Greece, mgiagkou@athenarc. gr, spip@athenarc. gr the EU. Against this background, the EU-funded project European Language Equality (ELE) has addressed the issue of Digital Language Equality (DLE) in Europe, with the intention of tackling the imbalances across Europe's languages, that have widened even further in the meantime, as explained in Chapter 4. ELE's contribution to advancing DLE in Europe hinges on a systematically developed and inclusive all-encompassing strategic research, innovation and implementation agenda (SRIA) and a related roadmap to drive forward much needed efforts in this direction (see Chapter 45). The present chapter describes the notion of DLE and the associated metric that are at the heart of these plans, and presents the DLE dashboard that visualises the digital support of each European language, so as to monitor the overall progress towards DLE in Europe, also in a comparative fashion across languages.
2012)  and CLARIN  (Hinrichs and Krauwer 2014). Additionally, META-NET, an EU Network of Excellence forging the Multilingual Europe Technology Alliance, was established and a group of projects (T4ME, CESAR, METANET4U, META-NORD) promoted and supported the development of Language Technologies (LTs) for all European languages  (Rehm and Uszkoreit 2012, 2013; Rehm et al. 2016). The EU project CRACKER (Cracking the Language  Barrier, 2015 Barrier, -2017) )  continued the work of META-NET, concentrating on additional strategy development and community building  (Rehm et al. 2020). The most recent EU-funded projects continuing efforts in this area were European Language Grid (ELG, Rehm 2023b) and European Language Equality (ELE,  Rehm et al. 2022) , which collaborated closely, leading to the development of the DLE Metric and the DLE dashboard presented in this chapter.
Related Work While our work on DLE focused specifically on the languages of Europe, it is located in a broader context of related recent efforts with a wider remit, which are briefly reviewed here to pinpoint issues of interest for the subsequent presentation of the definition of DLE, its metric and the dashboard. Joshi et al. (2020)  investigate the relation between the languages of the world and the resources available for them as well as their coverage in Natural Language Processing (NLP) conferences, providing evidence for the severe disparity that exists across languages in terms of technological support and attention paid by academic, scientific and corporate play-ers. In a similar vein,  Blasi et al. (2022, p. 5486)  argue that the substantial progress brought about by the generally improved performance of NLP methods "has been restricted to a minuscule subset of the world's approx.
Khanuja et al. (2022)  propose an approach to evaluate NLP technologies across the three dimensions of inclusivity, equity and accessibility as a way to quantify the diversity of the users they can serve, with a particular focus on equity as a largely neglected issue. Their proposal consists of addressing existing gaps in LRT provision in relation to societal wealth inequality. Khanuja et al. (2022)  lament in particular the very limited diversity of current NLP systems for Indian languages, and to remedy this unsatisfactory situation they demonstrate the value of region-specific choices when building models and creating datasets, also proposing an innovative approach to optimise resource allocation for fine-tuning. They also discuss the steps that can be taken to reduce the biases in LRTs for Indian languages and call upon the community to consider their evaluation paradigm in the interest of enriching the linguistic diversity of NLP applications.
Acknowledging that LTs are becoming increasingly ubiquitous, Faisal et al. (  2022 ) look into the efforts to expand the language coverage of NLP applications. Since a key factor determining the quality of the latest NLP systems is data availability, they study the geographical representativeness of language datasets to assess the extent to which they match the needs of the members of the respective language communities, with a thorough analysis of the striking inequalities. Bromham et al. (2021)  examine the effects of a range of demographic and socio-economic aspects on the use and status of the languages of the world, and conclude that language diversity is under threat across the globe, including in industrialised and economically advanced regions. This study finds that half of the languages under investigation faced serious risks of extinction, potentially within a generation, if not imminently.
This is certainly an extremely sombre situation to face up to, which calls for a large-scale mobilisation of all possible efforts by all interested parties to avoid such a daunting prospect, particularly in Europe, where multilingualism is recognised as an important part of diversity. Establishing a working definition of DLE, devising a metric to measure the situation of each European language with respect to DLE and implementing an interactive dashboard to monitor progress in this direction are vital elements of this large-scale endeavour.
With this in mind, the definition of DLE and the implementation of the DLE Metric discussed below are intended to accurately capture the needs and expectations of the various European languages, and especially the shortfalls with respect to being adequately served in terms of resources, tools and technological services in the digital age, so as to support the large-scale efforts to achieve DLE, also through data analytics and visualisation in the DLE dashboard. The definition of DLE drew inspiration, among others, from the META-NET White Paper Series  (Rehm and Uszkoreit 2012)  and from the BLARK concept (Basic Language Resource Kit,  Krauwer 2003) , which have been instrumental in assessing the level of technological support for specific languages, and in particular in identifying those that lag behind in the digital age and in encouraging the targeted interventions required to fill the gaps in LT support.
The setup and formulation of the metric are modular and flexible, i. e. , they consist of well-defined separate and independent, but tightly integrated quantifiers. In particular, the TFs were devised so as to be compatible with the metadata schema adopted by the European Language Grid cloud platform 2   (Labropoulou et al. 2020; Piperidis et al. 2023). The ELG cloud platform bundles together datasets, corpora, functional software, repositories and applications to benefit European society, industry and academia and administration, and provides a convenient single access point to LRTs for Europe's languages  (Rehm 2023a ). In addition, the definition of DLE and its associated metric have been designed to be transparent and intuitive for linguists, LT experts and developers, language activists, advocates of language rights, industrial players, policy-makers and European citizens at large, to encourage the widest possible uptake and buy-in to the cause of DLE across Europe.
In establishing the DLE definition and its associated metric, an effort was made for them to be founded on solid, widely agreed principles, but also striking a balance between a methodologically sound and theoretically convincing approach, and a transparent formulation. The rationale behind this approach was that the DLE definition and its metric should be easily understood and able to inform future language and LT-related policies at the local, regional, national and European levels in order to guide and prioritise future efforts in the creation, development and improvement of LRTs according to the SRIA and roadmap (see Chapter 45), with the ultimate goal of achieving DLE in Europe by 2030. Through data analytics and visualisation methods in the DLE dashboard (see Section 7), European languages facing similar challenges in terms of LT provision can be grouped together, and requirements can be formulated to support them in remedying the existing gaps and advancing towards full DLE.
Technological Factors In order to objectively quantify the level of technological support for each of Europe's languages, a number of TFs were considered. The following description presents their main categories, illustrating the breadth and diversity of the LRTs that they capture through the ELG catalogue  (Rehm 2023a; Piperidis et al. 2023; Labropoulou et al. 2020). In that regard, we assume that the ELG catalogue, with its more than 13,000 LRTs at the time of writing, provides a representative picture of the state of play of technology support of Europe's languages. The first category of TFs is based on the availability of LRs, i. e. , corpora, datasets or collections of text documents, text segments, audio transcripts, audio and video recordings, etc. , monolingual or bi-/multilingual, raw or annotated.
Weights and Scores The weights given to the feature values of the LRTs quantify their contribution to the DLE score with regard to the relevant TFs. The scoring system (see Tables  1  and 2 ) is based on the assumption that for any language some features of LRTs contribute more effectively to achieving DLE than others. Higher weights are assigned to feature values related to 1. more complex LRTs, e. g. , tools that process or support more than one modality, 2. more expensive and labour-intensive datasets or tools, e. g. , in terms of the effort required to build them, 3. more open or freely available datasets and tools, and 4. additional envisaged applications that could be supported. One guiding consideration in developing the DLE Metric, and especially in assigning the weights of the features and their values for the TFs, is to make the fewest possible assumptions about the (preferred or supposedly ideal) use-cases and actual application scenarios that may be most relevant to users.
In addition to Domain, another feature that appears both in LRs and tools and services is Conditions of use: the weights proposed for this feature of the TFs are identical for the corresponding values of Conditions of use across datasets and tools and services. In the case of (much) more restrictive licensing terms, lower weights are assigned than to liberal use conditions, so they contribute (much) less to the partial technological DLE score for the LRT in question, and therefore to the overall technological DLE score for the specific language.
Configuration of the Technological Factors Before coming up with the final implementation of the weighting and scoring system for the TFs (see Tables  1  and 2 ), we experimented with a range of different setups. We used the contents of the ELG catalogue as of early 2022, which at that time contained about 11,500 records, out of which about 75% were datasets and resources (corpora, lexical resources, models, grammars) and the rest were tools and services. These records contained multiple levels of metadata granularity. The ELG repository had been populated with LRTs following extensive efforts by a wide range of language experts and reflected the input of this community of experts, mobilised in ELE, to ensure comprehensive coverage, which is why we considered the ELG catalogue representative with regard to the existence of LRTs for Europe's languages, so it was used as the empirical basis for the computation of the technological DLE scores.
The ELG catalogue includes metadata for LRs and LTs. In ELG, each resource and tool/service has several features and associated values, based on the schemes presented in Tables  1  and 2. Each feature was initially assigned a tentative weight to calculate preliminary technological DLE scores of each language, comparing the resulting scores of a number of alternative preliminary setups. During this fine-tuning of the weights, we considered especially where each language stood in relation to the others and how their relative positioning changed as a result of assigning different weights to the various feature values. This was an efficient and effective method to gradually refine the setup of the TFs and propose the implementation of the weights in the scoring mechanism that was eventually adopted (see Tables  1  and 2 ).
Then, using the weights as specified in Table  1 , LRT1 is assigned the following score: Score LRT 1 = 5 + 1 + 2. 5 + (3 * 0. 25) + 1 + (1 * 0. 3) + 3. 5 = 14. 05 2. To compute the technological DLE score for language X (T echDLE LangX ) we sum up the Score LRT of all LRTs that support language X (LRT1, LRT2, …LRTN), i. e. , T echDLE LangX = N ∑ i=1 Score LRT i Similarly, any tool or service included in the ELG catalogue receives a partial score with the same procedure, on the basis of the weights presented in Table  2. As the ELG catalogue organically grows over time, the resulting technological DLE scores are constantly updated for all European languages. These scores can be visualised through the DLE dashboard (see Section 7), providing an up-to-date and consistent (i. e. , comparable) measurement of the level of LT support and provision that each language of Europe has available, also showing where the status is not ideal or not at the level one might expect.
As far as datasets are concerned, in particular, there could be benefits in setting a minimum size criterion to include LRs such as corpora or grammars in the computation of the technological DLE score, e. g. , to avoid using very small resources that cannot be realistically applied in actual technology development scenarios. However, it is difficult to establish arbitrarily what this minimum size threshold should be, also in recognition of the specifics of the languages of Europe. As a result, the decision was made not to set any minimum size requirement for LRs. The thinking behind this choice was that relatively small datasets are common in less-resourced languages, for particular domains, etc. , and there is the possibility to merge small datasets to create bigger ones that would, in fact, be useful, for instance in domain adaptation for MT, to mention but one example.