dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis The baseline characteristics of women were reported descriptively. Categorical variables were summarized using counts and percentages, and continuous variables were summarized using means and standard error of the mean. Fisher's exact test and the c 2 test were used to compare qualitative variables. Quantitative variables were compared using Student's t test. The uterine volume was compared according to the MAC regimens and according to hormonal impregnation using Student's t test. The quartile distribution of age at HSCT was used to assess its potential impact on the uterine volume. All statistical analyses were conducted using SPSS software. A P value of <. 05 was chosen for the statistical significance threshold.
DISCUSSION Until recently, only radiotherapy was known to induce damage on the uterus and have an impact on obstetric outcomes after cancer. Our study is the first to report by MRI in a homogeneous population of AL survivors a deleterious effect of HSCT after alkylating agent-based regimen on the myometrium and uterine volume. Our results complement data from 2 previous studies by Beneventi et al. (9)  and van de Loo et al. (10)  who also reported uterine damage after a high dose of chemotherapy; however, their results were obtained by pelvic ultrasound scan imaging in a heterogeneous group of cancer survivor women. In their study, Beneventi et al. (  9 ) compared 127 women, treated by HSCT during childhood, with 64 age-matched control women. Their population study included numerous types of malignant and nonmalignant hematologic diseases and solid tumors, leading to a heterogeneous population of post-HSCT survivors, exposed to various chemotherapy protocols.
We lack data to explain why the uterine volume would be less affected in case of chemotherapy exposure before puberty. Perhaps alkylating agents would be more cytotoxic on a pubertal growing uterus with differentiated myometrial cells. These results need to be confirmed by further studies. The strengths of our study were to have had access to the large French LEA database, with a homogeneous population of HSCT survivors with standardized MAC regimens, and the blinded MRI analysis in a single investigator center. The limitation of our study is that we have not collected enough data to correlate age at HSCT, MAC regimen, and uterine volume with the chance of spontaneous fertility and obstetric outcome. Despite the possibility of spontaneous pregnancy, there is a high incidence of POI and infertility after HSCT.
Acknowledgments: The authors thank all women who agreed to participate in this study. The authors also thank all radiologists from the  French University Teaching Hospitals  who have collaborated with the FERTILEA study:  Dr Pascale Siles  ( Marseille University );  Pr Pascal Rousset  ( Lyon University );  Dr Servane Le Lez-Soquet ;  Dr Laureline Monteau ;  Dr Eug enie Beraud  ( Rennes University );  Dr H el ene Chavassa  ( Toulouse University );  Dr Aur elie Delouche  ( Grenoble University );  Pr Madleen Chassang  ( Nice University );  Pr Jean-Francois Chateil  ( Bordeaux University );  Pr Val erie Laurent (Nancy University) ;  Pr Marianne Alison  ( APHP Robert Debr e-Saint Louis );  Dr M elanie Kass  ( CHU St-Etienne );  Pr Catherine Roy  ( Strasbourg University );  Dr Benoit Chauveau ;  Dr Constance Hordonneau ;  Dr Nicolas Rouel  ( Clermont-Ferrand University ); and  Pr Ingrid Millet (Montpellier University ).
Evaluating glacial change and the subsequent water stores in high mountains is becoming increasingly necessary, and in order to do this, models need reliable and consistent glacier data. These often come from global inventories, usually constructed from multi-temporal satellite imagery. However, there are limitations to these datasets. While clean ice can be mapped relatively easily using spectral band ratios, mapping debris-covered ice is more difficult due to the spectral similarity of supraglacial debris to the surrounding terrain. Therefore, analysts often employ manual delineation, a time-consuming and subjective approach to map debris-covered ice extents. Given the increasing prevalence of supraglacial debris in high mountain regions, such as High Mountain Asia, a systematic, objective approach is needed.
The current study presents an approach for mapping debris-covered glaciers that integrates a convolutional neural network and object-based image analysis into one seamless classification workflow, applied to freely available and globally applicable Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 interferometric coherence, and geomorphometric datasets. The approach is applied to three different domains in the Central Himalayan and the Karakoram ranges of High Mountain Asia that exhibit varying climatic regimes, topographies and debris-covered glacier characteristics. We evaluate the performance of the approach by comparison with a manually delineated glacier inventory, achieving F-score classification accuracies of 89. 2%-93. 7%. We also tested the performance of this approach on declassified panchromatic 1970 Corona KH-4B satellite imagery in the Manaslu region of Nepal, yielding accuracies of up to 88.
4%. We find our approach to be robust, transferable to other regions, and accurate over regional (>4,000 km 2 ) scales. Integrating object-based image analysis with deep-learning within a single workflow overcomes shortcomings associated with convolutional neural network classifications and permits a more flexible and robust approach for mapping debris-covered glaciers. The novel automated processing of panchromatic historical imagery, such as Corona KH-4B, opens the possibility of exploiting a wealth of multi-temporal data to understand past glacier changes.
Several studies show the upward expansion of debris cover  (Quincey and Glasser, 2009; Jiang et al. , 2018; Mölg et al. , 2019; Tielidze et al. , 2020) , with further expansion expected in the coming decades in the context of a warming climate  (Herreid and Pellicciotti, 2020). With the projected rates of increase in supraglacial debris cover, quantifying its extent and thickness is crucial for glacio-hydrologic models, as ignoring the presence of debris cover results in the overestimation of glacial retreat rates  (Ragettli et al. , 2016) , mass loss  (Rounce et al. , 2018) , and the underestimation of glacial water resource longevity  (Herreid and Pellicciotti, 2020). However, existing global supraglacial debris databases  (Scherler et al. , 2018; Herreid and Pellicciotti, 2020)  relied on heterogenous glacier inventories, namely, the Randolph Glacier Inventory (RGI v6) which only represents the glacier extents for the decade 2000.
, 2010; Nagai et al. , 2013; Paul et al. , 2013; Nuimura et al. , 2015; Mölg et al. , 2018) , or developed semi-automated approaches using varying combinations of satellite and geomorphometric datasets with varying degrees of success  (Paul et al. , 2004; Bolch et al. , 2007; Bhambri et al. , 2011; Racoviteanu and Williams, 2012; Lippl et al. , 2018). Novel approaches have emerged since 2010 to automate the mapping of supraglacial debris, namely, shallow architecture machine learning algorithms such as Support Vector Machine  (Huang et al. , 2014; Yousef et al. , 2020; Shukla et al. , 2022) , Maximum Likelihood Classifier  (Shukla et al. , 2010) , Artificial Neural Networks  (Karimi et al. , 2012) , and Random Forest Classifier  (Zhang et al. , 2019; Alifu et al. , 2020; Khan et al. , 2020; Lu et al.
, 2016) , which can be utilised to reduce misclassifications. Therefore, a hybrid mapping approach that integrates a CNN and OBIA into a single classification workflow holds high potential to delineate debris-covered glacier extents with high accuracy in an objective, systematic, and consistent manner. In this study, we present and evaluate an integrated CNN-OBIA approach for debris-covered glacier classification at regional scales using freely and globally available (2018-2019) Sentinel-2 multispectral, Landsat-8 thermal, Sentinel-1 coherence, and geomorphometric datasets. We test our approach over three domains in High Mountain Asia-the Khumbu, Manaslu, and the Hunza-which display varying topographies, debris-covered tongue surface morphologies and glacier flow dynamics. Furthermore, we adapt the CNN-OBIA approach and apply it to stereo Corona KH-4B satellite imagery in the Manaslu region of Nepal to assess the potential of the approach to derive debriscovered glacier outlines from the 1970s.
Transfer learning Training CNNs in new regions is a time-consuming process that requires significant computational resources and is dependent on high-quality glacier inventories. Transfer learning offers a means to significantly reduce computing time and enhance network performance in regions where data is scarce  (Kunze et al. , 2017)  by pretraining network weights in one region and transferring them to out-of-sample regions. Transfer learning allows the streamlining of large-scale glacier inventory creation. The transfer of a pretrained CNN to in-sample imagery, i. e. , imagery included in the training dataset, for debris-covered glacier classification was successfully performed by  Xie et al. (2020) ; however, the application of transfer learning to out-of-sample imagery, i. e. , imagery outside the training dataset, still needs testing.
In addition, a second Sentinel-2 image acquired approximately 1 year apart (November 2018, August 2019) was used to derive a surface velocity dataset for each domain based on normalised crosscorrelation (NCC) using the IMCORR Feature Tracking module within SAGA GIS 2. 3. 2. The surface velocity datasets were used in conjunction with Worldview-2 imagery accessed through ArcGIS Online to create the validation data for each study region. We also obtained two Sentinel-1 single look complex (SLC) images in interferometric wide (IW) swath mode for the Hunza (  2018 ),  Khumbu and Manaslu (2019). Images were separated by a temporal baseline of 12 days and were co-registered using cross-correlation with the INSCOREG algorithm within PCI Geomatica CATALYST. The resulting image stack was used to generate a coherence raster which was converted to true ground range and orthorectified using the ALOS World 3D (AW3D30) DEM  (Tadono et al.
Geomorphometric data Elevation data used for the recent period was based on the AW3D30 DEM (30 m), produced using ALOS high-resolution stereo imagery acquired between 2006 and 2011, with an accuracy (root mean squared error, RMSE) of 6. 84 m over High Mountain Asia  (Liu et al. , 2019). Although other DEMs were available, some of higher resolution, such as the HMA DEM  (Shean, 2017) , the AW3D30 has consistent coverage, with the lowest RMSE of the open access 30 m DEMs over High Mountain Asia and does not contain large data voids like the HMA DEM  (Liu et al. , 2019). We used the AW3D30 dataset to extract elevation and five geomorphometric datasets (slope angle, profile curvature, planform curvature, aspect, and shaded relief), which were subsequently used as layers in the classification procedure.
The justification for each DEM derivative is given in Table  2. All satellite and geomorphometric datasets were projected to UTM (zone 45 N for the Khumbu and Manaslu domains and UTM zone 43 N for the Hunza domain). For all datasets, pixel values were normalised between 0 and 1 and converted to 32-bit floating rasters using the SCALE algorithm within PCI Geomatica CATALYST. The Landsat-8, Sentinel-1 and AW3D30 datasets were bilinearly resampled to 10 m to match the spatial resolution of the Sentinel-2 imagery.
Corona datasets To test our approach on panchromatic images from the 1970s, we used two Corona KH-4B images covering the Ponkar and Hinang subregions of the Manaslu domain. The Corona KH-4B satellite was developed as part of the US Keyhole (KH) space reconnaissance programme, which acquired ~800,000 high-resolution (1. 8-7. 5 m) panchromatic images of Earth's surface between 1960 and 1972  (Dashora et al. , 2007). The Corona scenes were made available as fully processed orthomosaics at 2 m spatial resolution along with corresponding DEMs, extracted from these scenes using ERDAS Imagine, at 10 m spatial resolution (see  Robson et al. , 2018; Racoviteanu et al. , 2022a). For details of the Corona imagery processing, the reader is directed to these papers. Elevation, slope angle, profile curvature, planform curvature, shaded relief and aspect geomorphometric datasets for the 1970s decade were generated from the 1970 Corona DEMs (10 m).
Reference vector data generation For Sentinel and Corona imagery, we targeted the classification of seven predominant land-cover classes present in the imagery: supraglacial debris, clean ice, snow cover, lakes, vegetation, shadows, and non-glacial material. In order to train the CNN to recognise each of these classes, we generated a reference vector dataset for each class. The reference vector dataset was then used to automatically generate the training dataset. To avoid the laborious and timeconsuming process of manually selecting sample locations to generate a high-quality training dataset, we used an automated reference vector data generation method adapted from  Alifu et al. (2020)  for application to multi-class output CNNs. As a first step, we generated reference polygons for each land-cover class.
1 lake dataset which was generated by thresholding the NDWI  (Shugar et al. , 2020). Vegetation polygons were generated using the NDVI derived from bands 4 and 8 with a threshold > 0. 3. Shadow reference polygons were generated using the lowest 5% reflectance values in the blue Sentinel-2 multispectral band. The remainder of the unclassified area within the domain was assigned as non-glacial material. Internal buffers were applied inside the reference polygons generated for each land-cover class so that the resulting reference vector points were not located near the boundary of land-cover classes. The buffer size (15-100 m) was selected according to the area coverage of the land-cover classes and the spectral similarity between neighbouring land-cover classes (Figure  3 ). Following this, we created reference vector points within the buffered reference polygons for each land-cover class to generate the reference vector dataset.
CNN inputs We used all the available datasets as inputs for the CNN. The inputs consisted of ten Sentinel-2 bands (blue, green, red, vegetation red edge I-IV, NIR, SWIR I-II), Landsat-8 TM10, Sentinel-1 coherence, and six AW3D30 geomorphometric datasets. We used coherence to aid the identification of areas that have undergone glacier motion, deformation, or changing surface conditions. We employed TM10 to aid the differentiation between supraglacial and paraglacial debris based on land surface temperature differences. In addition, we included NDSI, NDWI and NDVI as inputs to help further differentiate between landcover classes. The Corona classification used seven inputs: the panchromatic Corona image and the six geomorphometric datasets derived from the Corona DEM.
Training datasets To generate the training dataset from the reference vector dataset, as a first step, a 10 m buffer was created around each reference vector point created for each land-cover class presented in Section 4. 2. The buffered vector points were then used to mark the locations for labelled training samples to be generated from for each land-cover class. We used landcover prevalence and spectral signatures to determine the number of labelled training samples for each land-cover class, following  Robson et al. (2020)  (Table  3 ). The training dataset was composed of 69,500 samples (all classes cumulated) for each domain. For the Corona implementation, we also used a 10 × 10 m pixel buffer and generated 7,000 labelled training samples for each land-cover class, creating an accumulative training dataset of 49,000 samples.
The CNN trains in ~5 min on a computer with 64 GB RAM, an Intel Core i7 processor, and an NVIDIA Quadro RTX 4000 graphics card. The CNN outputs a probability heatmap for each land-cover class in the training dataset. The outputted probability heatmaps were smoothed with a 7 × 7 Gaussian filter to reduce blurred boundary effects. We only employed the supraglacial debris heatmap for the subsequent debris-covered glacier OBIA classification, the other land-cover classes were classified according to fixed satellite datasets thresholds.
OBIA classification In order to simplify the ruleset and ensure approach transferability among the three domains, we minimised the reliance of the OBIA ruleset on thresholds such as coherence, elevation and surface temperature that could differ between the supraglacial environments in the domains. Clean ice is mapped as a by-product of the OBIA supraglacial debris mapping procedure for both the Sentinel and Corona datasets. Although clean ice is not the focus of our paper, assessing the accuracy of the clean ice classification allows for our approach to be compared with previous approaches and for the overall inventory-creating potential of the approach to be assessed. A four-level image segmentation was performed using the multiresolution segmentation (MRS) algorithm (Table  4 ). MRS applies a mutual best-fitting approach to merge pixels into homogeneous image-objects  (Baatz and Schape, 2000).
MRS requires three parameters to be selected: scale, shape, and compactness. Segmentation quality heavily depends on the initial selection of these parameters  (Rastner et al. , 2014; Robson et al. , 2015). Land-cover classes were classified according to fixed probability heatmap, spectral, geomorphometric, coherence and contextual thresholds (Figure  2 ). The resulting clean ice and supraglacial debris outlines were smoothed using pixel-based object resizing with an 11 × 11 surface tension window and a relative area threshold of < 50% for both growing and shrinking operations and were then exported as shapefiles. The OBIA refinement for the Corona imagery was conducted in a similar manner as for the Sentinel data with the same segmentation scale parameter values as those used for the Sentinel classification.
Shape and compactness were adjusted to account for the lack of pixel value variability. In the absence of multispectral bands, higher weightings were given to slope and curvature datasets. Landcover classes were classified according to fixed probability heatmap, slope and contextual thresholds given in Table  5. Finally, image-objects were smoothed with pixel-based object resizing using 11 × 11 surface tension windows. Shrinking and growth occurred if supraglacial debris pixels occupied < 50% of the window.
Out-of-sample transfer learning methodology As a separate experiment, we assessed the suitability of transfer learning on out-of-sample imagery. For this, we used the same network set-up as the one employed for the Sentinel-2 classification. We trained a CNN with training samples produced in one of the domains and transferred and applied it to the other two domains, iteratively. This process was performed for each domain. For example, the Khumbu CNN was trained using satellite and geomorphometric datasets from this domain, and the network was then transferred and applied to the satellite and geomorphometric datasets in the Hunza and Manaslu domains, which had not been used in the training dataset. Following the CNN classification, we performed the OBIA classification as described in Section 4.
Accuracy assessment To assess the accuracy of the approach, we created a manually delineated glacier inventory derived from high-resolution 2016 Worldview-2 imagery, Sentinel-2 imagery, and Sentinel-1 coherence data. We only found minor changes in supraglacial debris extents between the Worldview-2 and the Sentinel-2 acquisition dates (2016-2019); these were insignificant at the 10 m resolution scale of the Sentinel-2 imagery. We used a surface velocity raster to separate the active ice from stagnant glacier tongues in the manual inventory. Ice flowing at velocities <2. 5 m/a -1 was assumed to be stagnant following  Scherler et al. (2011)  and  Shukla and Garg (2020). We used the remaining 70% of the debris-covered glacier polygons that were not employed to generate the reference vector dataset to assess the accuracy of the approach.
Corona classification The extent of clean ice was underestimated in both images (Figure  8 ). Underestimation percentages ranged from 8. 7% to 14. 8%. In total, 87. 8 out of 96. 2 km 2 of clean ice was mapped in the Hinang area and 75. 4 out of 88. 5 km 2 in the Ponkar area of the Corona image. The IOU accuracies of 76. 2% (Hinang) and 79. 4% (Ponkar) indicate satisfactory agreement between CNN-OBIA outlines and the validation dataset. Clean ice was mapped in the two images with F-score accuracies of 81. 0% in the Hinang area and 83. 7% in the Ponkar area. Precision and recall accuracies indicate that omission errors were more common than commission errors. Clean ice sections had a mean slope gradient of 25. 6 °in the Hinang area and 24. 7 °in the Ponkar area image. CNN-OBIA identified 14 of 18 debris-covered tongues featured in the validation data across the two Corona images.
Employing OBIA thresholding allows these misclassifications to be removed through masking or utilising contextual information in the classification. Since OBIA is capable of classifying land-cover classes based on empirically thresholding low-level features, commonly commissioned land-cover classes such as waterbodies and vegetation can be masked out from further analysis, preventing the inclusion of CNN misclassifications in the final delineated debris-covered glacier area. This function may be especially advantageous when working with multi-class output CNNs, label noise in the training dataset, or unbalanced training datasets  (Fernández et al. , 2018). Employing OBIA contextual-based rules such as distance from clean ice, combined with shape rules such as minimum area coverage also allows misclassifications caused by the similarity between landforms, such as mass movement deposits and ice-cored moraines, to be rectified (Figure  5 ).
The CNN could not classify these debris-covered regions due to a lack of intra-class variation within the supraglacial debris samples. These regions were likely not included in the labelled training samples because the reference dataset creation method prevents labelled samples from being extracted from areas near potentially problematic land-cover boundaries. Thus, the CNN was not trained to recognise supraglacial debris on steep slopes and termini as part of debris-covered tongues, leading to their exclusion from the glacier outlines. Omission errors were also caused by shadows on the margins of debris-covered tongue surfaces; however, this omission accounted for only 0. 15% of the total debris-covered area across the three study regions (0. 61 out of 393. 9 km 2 ).
In the absence of multispectral data, the Corona-based classification relied heavily on the panchromatic band, causing paraglacial landforms that exhibit high panchromatic values and geomorphometric properties similar to supraglacial debris or clean ice, such as river channels, frozen lakes, and debris flows, to be frequent sources of commission error. Clean ice and supraglacial debris were also frequently misclassified as one another. Furthermore, we found that the surface of the debris-covered tongues reflected in the high-resolution geomorphometric datasets shared similar textures with the lateral moraines, leading to the inclusion of lateral moraines in the delineated supraglacial debris extent and adding an average of 8. 5% to the total delineated debris-covered area across the two Corona images.