dataesr/scientific-paragraphs-categorization huggingface dataset:
Cropland Boundary Data Cropland boundary data were provided by the annual MODIS MCD12Q1 V6 product  [26]. MCD12Q1 is a land cover-type yearly global product from the MODIS sensors. This product provides global land cover classification data with a 500 m spatial resolution. The MCD12Q1 dataset is generated annually and contains information on various land cover types, such as urban areas, grasslands, croplands, wetlands, and forests. The MCD12Q1 product uses multiple classification schemes, and these classification schemes help scientists and decision-makers to better understand land cover dynamics, monitor changes, and assess the impacts of land use and land cover change on ecosystems, the climate, and human-environment interactions. We used the Annual International Geosphere-Biosphere Programme classification (land cover type 1) as a mask to select the cropland pixels.
After completing each phase of fieldwork, the team returned to the laboratory to process and verify the collected field data. Once the data had been validated, the subsequent fieldwork phase was initiated. Prior to initiating field experiments, we roughly selected sampling sites indoors using high-spatial-resolution imagery (i. e. , Sentinel-2) and approximate predicted crop types, favoring areas that were as large as possible, minimally disturbed, and easily accessible, while ensuring that all sampling sites were evenly distributed throughout the study area. Once all sampling points were identified, we used a portable global positioning system (GPS) to measure the geometric locations, validate the predetermined crop types, and document primary information for each cropland. GPS measurements were taken as close to the center of the field as possible, and preliminary data were recorded concurrently.
Data Preprocessing We assessed the potential of multi-angle observations for crop classification by utilizing both single-and multiple-angle-observation remote sensing data. The single-angle observation type was the MODIS nadir reflectance (MCD43A4 V6), referred to as NR (nadir reflectance). The multiple-angle observation type was the MODIS kernel-driven model parameters (MCD43A1 V6), referred to as MF (multi-angle observation factors). We restricted the data to cover Jilin Province using one spatial filter and limited it to 2017 using one date filter. We employed all available MODIS NR and MF data from two distinct periods: the growth stage (day of year: 170-230) and the harvest stage (day of year: 231-291)  [35]. Within each of these periods, we took the medians as temporal metrics for all band data (i.
Accuracy Comparison among Crop Types Tables  4  and 5  show the producer accuracy (PA) and user accuracy (UA) measures of the training and validation datasets, respectively. Producer accuracy indicates the number of pixels properly classified in a specific class as a proportion of the total number of pixels in that class in the reference data. Producer accuracy assesses the classifier's ability to accurately identify a specific class, indicating the probability that a certain class in the reference data (ground truth) is correctly classified in the image classification. This metric is also referred to as the commission error. A high producer accuracy indicates that the classifier effectively identifies a specific class, while a low producer accuracy suggests that the classifier may be misclassifying that class.
User accuracy indicates the number of correctly classified pixels for a given class as a proportion of the total number of pixels in the image classification that are classified as that class. This metric is also known as the omission error. User accuracy evaluates the reliability of the classification for a specific class, indicating the probability that a certain class in the classified image corresponds to the same class in the reference data (ground truth). A high user accuracy indicates that the classification results for a specific class are reliable, while a low user accuracy suggests that the classifier may include pixels from other classes in the specific class  [43]. The training dataset held higher PA and UA values than the validation dataset did, with mean values of 68. 22% and 46.
RF is famous for effectively handling large datasets with high dimensionality, providing a good balance between bias and variance, reducing the risk of overfitting by aggregating multiple decision trees, handling missing data and maintaining accuracy, and estimating feature importance. However, RF can be slow to train and predict with large trees and poor performance on imbalanced datasets  [58, 59]. (2) Classification and Regression Trees Classification and regression trees (CART)  [60]  is a decision tree learning technique that recursively splits the input data into subsets on the basis of the values of the input features, aiming to create pure leaf nodes (i. e. , nodes containing only one class for classification or similar values for regression). CART is famous for capturing non-linear relationships and handling both categorical and continuous data.
Data Availability Statement: The Google Earth Engine can be assessed through the website (https: //explorer. earthengine. google. com, accessed on 23 May 2023). The MCD43A1 product can be assessed through the website (https://developers. google. com/earth-engine/datasets/catalog/MO DIS_006_MCD43A1, accessed on 23 May 2023). The MCD43A4 product can be assessed through the website (https://developers. google. com/earth-engine/datasets/catalog/MODIS_006_MCD43A4,.
Paleomagnetic observations Our intention here is not to exhaustively detail the presently available Ediacaran paleomagnetic data, as have already been amply dissected by several other contributions (e. g. Abrajevitch and Van der Voo, 2010; McCausland et al. , 2007; Meert, 2014; Robert et al. , 2017) , but rather to highlight specific constraints that characterize the problem and enable consideration of the alternative hypotheses. We focus on relatively rich datasets from Laurentia, Northwest Africa and Baltica (Table  1 ), but note that multiple Ediacaran paleomagnetic data are also available from several other continents, including Australia (e. g. Kirscher et al. , 2021) , Rio de la Plata (e. g. Rapalini et al. , 2021)  and Siberia (e. g. Vinogradov et al. , 2023). A striking characteristic of the datasets from each of these continents is that their paleomagnetic poles (hereafter 'paleopoles') appear to form two or more populations that are disparate in space, but very close or even indistinct in age (Fig.
3e ). The dataset from Baltica further differs from that of Laurentia and Northwest Africa in that the reliable paleopoles are mostly derived from sedimentary rocks. The only reliable pole from an igneous unit is the oldest one-from the 616 ± 3 Ma Egersund dikes (ED)  (Walderhaug et al. , 2007). From there, the APWP swings >65 • to the next youngest poles reported from mid-Ediacaran sedimentary rocks in the South Urals (BK, KF)  (Lubnina et al. , 2014). The APWP subsequently swings ~75 • between 585 and 573 Ma before swinging back ~90 • by 555 Ma  (Levashova et al. , 2015). A final ~90 • swing in the APWP then occurs between 550 and 500 Ma, although the middle and late Cambrian segments of the path are poorly defined  (Meert, 2014). Again, these directional changes imply rates of APW in excess of 4 • Ma -1 (Fig.
Note that the Ediacaran paleopoles from both Laurentia and Northwest Africa yield minimum drift rates that exceed our linear plate speed limit (25 cm a -1 ), and the paleopoles from Baltica include rates of APW that exceed the corresponding drift-related APW speed limit (4. 5 • Ma -1 ) (Fig. 3 ). Furthermore, as individual paleomagnetic data do not constrain longitude, rates of plate motion derived from simple comparisons between paleomagnetic data are minima (Fig. 5b ; although total plate motion estimates are potentially recoverable from very rich paleomagnetic datasets  (Gallo et al. , 2022; Gordon et al. , 1984; Rose et al. , 2022 ))-APW rates close to this speed limit may thus already reflect 'impossibly fast' differential plate motions. Given the discrepancies between the paleomagnetic observations and these predictions, it presently seems unlikely that the paleomagnetic data purely reflect fast plate motions.
Widespread data corruption This hypothesis contends that many Ediacaran paleomagnetic data have been compromised by one or more unrecognized pathologies, such as remagnetization, which can occur when rocks are subjected to high temperatures (thermal remagnetization), strong magnetic fields (e. g. lightning strikes) and/or are chemically modified (resulting in the alteration of primary magnetic minerals or allowing the growth of new ones). Additional pathologies include inclination shallowing, inaccurate structural restorations, vertical axis rotations, and erroneous age estimates. This is arguably the most conservative of the four hypotheses, as it implies that records of a well-behaved GAD field and 'moderate' (Phanerozoic-like) rates of plate drift and true polar wander have simply been obscured by a veil of data bias.
Unsurprisingly, this hypothesis has been the default for many years, but it is becoming increasingly harder to maintain in the face of repeat studies and growing evidence that (at least some of) the enigmatic directions represent primary magnetizations of high-fidelity. Nevertheless, there remain legitimate, lingering questions about specific aspects of numerous key constraints. To this point, it is worth noting that the original hypothesis of an Ediacaran equatorial dipole  (Abrajevitch and Van der Voo, 2010)  was formulated in part from magnetic directions from the Fen complex of Baltica that have long been suspected (although not proven) to be Permo-Triassic remagnetizations  (Meert, 2014). For many common paleomagnetic afflictions, the notion of a systemic Ediacaran 'infection' (that is, the widespread corruption of the data by any one of the aforementioned pathologies) can be summarily dismissed.
For example, whether inclination shallowing is prevalent among Ediacaran sedimentary units is rather irrelevant since the Ediacaran paleopoles from Laurentia and Northwest Africa are derived from igneous rocks. Similarly, although some results could have suffered from unrecognized vertical axis rotations, this cannot explain the distinct directional populations in the Laurentian or Northwest African datasets, which predominantly differ in inclination. Data pathologies are not restricted to the spatial dimension, and the apparently rapid rates of APW could be attributed to systematic age assignment errors. This is an important concern for Ediacaran sedimentary sections, especially those devoid of fossils and interbedded volcanics, but the paleopoles from Laurentia and Northwest Africa are derived from igneous units that have been directly dated (mostly by U-Pb methods).
The ages of several Ediacaran sedimentary-based poles from Baltica are likewise constrained by interbedded tuffs that have been isotopically dated. Pervasive age errors among these paleomagnetic data are thus unlikely. If Ediacaran paleomagnetic data were not universally corrupted by a single mechanism, perhaps multiple data pathologies together conspired to corrupt the global dataset. An obvious challenge to this hypothesis is the question of why Ediacaran paleomagnetic data should be more susceptible to data pathologies than data from earlier or later times (although perhaps the sparse data from earlier times are less reliable than we think?). One possibility is that the Ediacaran magnetic field was especially weak and yielded feeble primary magnetizations more easily masked by any subsequent partial remagnetizations.
Predictions & appraisals This hypothesis fundamentally differs from the others in that it does not necessarily appeal to a single mechanism. Indeed, potential problems with paleomagnetic data are legion, and this hypothesis allows for any combination of them. This may give the sobering impression that we cannot execute tests of this hypothesis without exhaustively scrutinizing every aspect of every pole, one-by-one. Fortunately, this is not the case. Although the identification of a specific pathology in any constraint certainly requires such a meticulous approach, we can more easily evaluate the possibility that the global dataset has been pervasively corrupted by random biases according to how the directions vary in time and space (Fig. 6  and Supp. Figs. S2, S3 ). Directional changes observed at remote locations are not expected to be temporally correlative, nor will their spatial evolution follow a systematic pattern.
Considered in isolation, the rapid directional changes observed in the datasets from Laurentia, Northwest Africa and Baltica appear to conform to some of these expectations. However, the broadly similar timing of the directional excursions, and their similar geometry (both in length and orientation, following a geologically-informed relative reconstruction of the continents; Fig. 4 ) are at odds with these expectations. Because the largest directional swings among the Ediacaran paleopoles are dependent on a handful of results, it is instructive to inspect some of these and their potential shortcomings in greater detail. For the data from Laurentia, much discussion has focused on the nature of the steep vs. shallow components found to be co-existing in several Ediacaran units  (Bono and Tarduno, 2015; Halls et al.
A critical inference is that it is highly doubtful that widespread remagnetization could explain all of the steep or all of the shallow directions. After all, field tests support steep primary directions in the Grenville dikes and Callander complex (where there is no shallow counterpart) and shallow primary directions in the Sept-Îles intrusion and Skinner Cove volcanics (where there is no steep counterpart). And as demonstrated above (section 2), any combination of steep and shallow directions from the assembled Ediacaran data from Laurentia will effectuate extremely rapid APW (> 4 • Ma -1 ; Fig. 3b ). Thus, while there are undoubtedly problems with unrecognized bias among the extant Ediacaran paleomagnetic data, it is not straightforward to ascribe the larger problem to magnetic fidelity issues alone.
We do not wish to dwell on interrogations of individual data, but a brief detour to Baltica is useful to consider this hypothesis from another perspective. In contrast to the dataset from Laurentia, the Ediacaran data from Baltica come almost exclusively from sedimentary rocks. One might thus suspect that unrecognized inclination shallowing could be distorting the directional dataset. However, the disparate directional groups differ predominantly in declination, and so cannot be explained as inclination bias (Fig. 3f ). On the other hand, declination differences could result from differential vertical axis rotations. Given that the single 'anomalous' late Ediacaran pole from Baltica comes from the structurally disrupted continental margin in the South Urals (Basu Fm; BA in Fig. 3e, f ), there is good reason to be skeptical of this result  (Meert, 2014).
, 2020). Thus, while the argument for the Basu Fm. pole being representative of the craton in the late Ediacaran is not iron-clad, it is not readily dismissed as fallacious either. However, if the Basu Fm. pole is recognized as flawed, the Baltican dataset would be 'cleansed' of aberrant late Ediacaran data (although fast APW would still be implied by early Ediacaran and Cambrian constraints)-so this dataset provides a more compelling case in support of the hypothesis of data corruption than that of Laurentia.
Implications Initially, this hypothesis might seem to bear the least significant ramifications. After all, it could allow the GAD hypothesis to prevail, and moderate plate motion and true polar wander rates could be revealed beneath a veneer of bias. But the realization that the present Ediacaran dataset is widely compromised by (as-yet) unrecognized artefacts implies major shortcomings in our ability to identify pathological data with presently standard instrumentation and methodologies. This would pose challenges to our confidence in paleomagnetic interpretations drawn from regions of space and time where the data are sparse and cannot otherwise be independently evaluated.
1e )  (Hounslow et al. , 2018). An outstanding question is whether the reversal rate intrinsically varies with the strength and stability of the dipole  (Kulakov et al. , 2019) , but  Gallet and Pavlov (2016)  have suggested that these exceptionally high rates may represent a distinct 'hyperactive' state of the geodynamo. Despite this evidence that the Ediacaran field was weak and hyperactive, it isn't straightforward to ascribe its complex paleomagnetic data to frequent transitional directions. One reason is because transitional directions still appear to constitute the minority in temporally nearcontinuous datasets: even in the magnetostratigraphic record from the late Ediacaran Zigan Fm. (≥ 20 rpMa),  Bazhenov et al. (2016)  and  Levashova et al. (2021)  estimated the fraction of transitional directions to comprise only ~10% of the dataset.
A second issue relates to the apparent consistency among the observed paleopole groups (Fig. 4 )irrespective of which is deemed 'transitional'-because transitional records from the last few millions of years appear highly complex and whether they exhibit any statistically meaningful VGP corridors or clusters remains disputed  (Love, 2000; McFadden et al. , 1993; Valet and Fournier, 2016). If the GAD was instead temporarily overpowered by a non-GAD field (e. g. an equatorial dipole), there must have been a transitional period between those two states. Given the timescales associated with secular variation and polarity reversals, this transition could be expected to be very rapid, resulting in rates of APW ≫ 10 • Ma -1 (Fig. 9  and Supp. Figs. S2, S3 ). If the non-GAD field was specifically dominated by an equatorial dipole, it could explain orthogonal directional datasets, as the position of the geomagnetic pole would shift by 90 •.
The first is observational: the latest Ediacaran to early Cambrian paleomagnetic data from Baltica suggest another directional excursion occurred at that time, but no indication of this has yet been recognized in Laurentia or Northwest Africa. Is this signal from Baltica reliable? If so, does a second directional excursion remain to be discovered among the other continents? The second challenge is theoretical: how could an equatorial dipole remain pinned for long enough so as to be detected from such sparse paleomagnetic records? These questions present important opportunities for further investigation.
Our analysis has revealed that fast plate motions are unlikely to be the main cause of the complex paleomagnetic data if the plate speed limit is on the order of 25 cm a -1 , but this theoretical limit-and how it may have varied through time-remains to be rigorously established. Widespread data corruption also appears unlikely to be able to entirely explain the enigmatic dataset, given the present collection of field test results and the observation that possibly spatiotemporally correlative directional changes are recorded on several distinct plates. However, continued scrutiny of the individual magnetic records, and especially  novel studies of their fidelity, is warranted. The spatiotemporal APW pattern evident from the paleomagnetic data considered here is most consistent with the predictions made by the TPW and anomalous field (non-GAD) hypotheses, but additional high-resolution paleomagnetic data paired with precise geochronological constraints will be needed to further test (and distinguish) them.
Temporally continuous directional datasets would be especially valuable. This presents a challenge, because although sedimentary successions can provide such temporally continuous records, they are often difficult to date. Fortunately, as the Ediacaran directional changes are of large amplitude (~90 • ), a temporal resolution of ~10 Ma should be sufficient to distinguish between processes rate-limited by mantle viscosity and those which are not (Fig. 10 ). It is possible that more than one hypothesized mechanism operated at the same time. Indeed, some combination of plate motion, TPW, data corruption and non-GAD field behavior (i. e. secular variation) has occurred throughout the Phanerozoic, and the intensification of one of these processes in the Ediacaran could have resulted in amplification of another.
Although a number of alternative paleogeographic models for Ediacaran time have been published, they invariably arbitrarily dismiss some subset of the paleomagnetic data or otherwise struggle to explain them, and so at best these models should only be considered as relative paleogeographic models. However, if the nature of the rapid Ediacaran paleomagnetic directional changes can be established, the paleomagnetic data can be applied to build quantitative Ediacaran paleogeographic models with absolute paleolatitude. A global-scale, quantitative Ediacaran spatial framework is critically needed to resolve a series of major tectonic events-including the final dispersal of Rodinia and the assembly of Gondwana-to test the hypothesis of a fleeting late Ediacaran-early Cambrian supercontinent (Pannotia), and to contextualize the immense climatic, geochemical and biological changes that were occurring on Earth's surface across this period.
If hypotheses 1, 3 or 4 (or some combination therein) are validated, the entire paleomagnetic dataset could be practically useful for paleogeographic application, whereas the validation of hypothesis 2 would require filtering to remove corrupted data. The validation of hypothesis 3 and/or 4 would further enable the quantitative determination of relative longitude in deep-time, which is otherwise not possible prior to the breakup of Pangea in the Early Jurassic. The validation of hypothesis 4 would also grant important new insights into the evolution of the geodynamo and the core, with relevance for ongoing debates concerning the timing of ICN. The testing of these four hypotheses is thus of central importance to understanding this critical transition from the Precambrian to the Phanerozoic, with implications potentially encompassing all the envelopes of the planet, from core to atmosphere.
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant agreement No. 101043844  to M. D. ) and from the  Research Council of Norway (RCN) through its Centres of Excellence funding scheme , project  223272  ( Centre for Earth Evolution and Dynamics ) and project  332523  ( Centre for Planetary Habitability ). Nicholas Swanson-Hysell  and  David Evans  provided insightful reviews that improved the manuscript, and we thank  Douwe van Hinsbergen  for editorial handling.
With the growing size of consolidated analytical chemistry databases, chemometrics still require methodologies to 1) provide accurate predictions 2) extract pertinent knowledge or offer useful insights on measurements 3) combine heterogeneous or high-dimensional data sources. When the number P of variables (samples) is far greater than the number of observations (signals) N (P N ), naive statistical models risk overfitting. This notably happens in standard least squares optimizations. Dimension reduction techniques are generic approaches to deal with high dimensionality. They include projection methods or variable selection algorithms. Commonly used strategies start with PCA/PCR (principal component analysis/regression), performed only on explanatory variables in X. They however do not incorporate information held by the response Y.