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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 11 new columns ({'label_source', 'agreement_with_prev', 'K', 'mean_conf', 'epochs', 'test_acc', 'test_error', 'subset', 'arch', 'time_s', 'eta'}) and 20 missing columns ({'s', 'theory_R', 'experiment', 'survival', 'sim_std', 'p', 'sigma2', 'lam', 'rho', 'T', 'trials', 'tstar_sim', 'tau', 'theory_B', 'sim_risk', 'tstar_theory', 'kappa', 'U_shape', 'r2', 'theory_V'}).
This happened while the csv dataset builder was generating data using
hf://datasets/evalstate/denoising-forgetting-linear-repro/trajectories/cifar_trajectory.csv (at revision 8b8cf2775ba6d998671aa22290082bc7b2082957), ['hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/all_linear_curves.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/cifar_trajectory.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/cifar_trajectory_ep15.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/fig1b_ushape.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/fig3a_anisotropy.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/igcv_vs_risk.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/risk_decomposition.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/stopping_criterion_summary.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
test_acc: double
test_error: double
mean_conf: double
agreement_with_prev: double
t: int64
eta: double
n: int64
K: int64
epochs: int64
arch: string
seed: int64
label_source: string
subset: int64
time_s: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1846
to
{'experiment': Value('string'), 's': Value('float64'), 'rho': Value('float64'), 'p': Value('int64'), 'n': Value('int64'), 'r2': Value('float64'), 'sigma2': Value('float64'), 'lam': Value('float64'), 'T': Value('int64'), 'trials': Value('int64'), 'seed': Value('int64'), 't': Value('int64'), 'sim_risk': Value('float64'), 'sim_std': Value('float64'), 'theory_R': Value('float64'), 'theory_B': Value('float64'), 'theory_V': Value('float64'), 'tstar_theory': Value('float64'), 'tstar_sim': Value('float64'), 'U_shape': Value('bool'), 'survival': Value('float64'), 'kappa': Value('float64'), 'tau': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 11 new columns ({'label_source', 'agreement_with_prev', 'K', 'mean_conf', 'epochs', 'test_acc', 'test_error', 'subset', 'arch', 'time_s', 'eta'}) and 20 missing columns ({'s', 'theory_R', 'experiment', 'survival', 'sim_std', 'p', 'sigma2', 'lam', 'rho', 'T', 'trials', 'tstar_sim', 'tau', 'theory_B', 'sim_risk', 'tstar_theory', 'kappa', 'U_shape', 'r2', 'theory_V'}).
This happened while the csv dataset builder was generating data using
hf://datasets/evalstate/denoising-forgetting-linear-repro/trajectories/cifar_trajectory.csv (at revision 8b8cf2775ba6d998671aa22290082bc7b2082957), ['hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/all_linear_curves.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/cifar_trajectory.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/cifar_trajectory_ep15.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/fig1b_ushape.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/fig3a_anisotropy.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/igcv_vs_risk.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/risk_decomposition.csv', 'hf://datasets/evalstate/denoising-forgetting-linear-repro@8b8cf2775ba6d998671aa22290082bc7b2082957/trajectories/stopping_criterion_summary.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
experiment string | s float64 | rho float64 | p int64 | n int64 | r2 float64 | sigma2 float64 | lam float64 | T int64 | trials int64 | seed int64 | t int64 | sim_risk float64 | sim_std float64 | theory_R float64 | theory_B float64 | theory_V float64 | tstar_theory float64 | tstar_sim float64 | U_shape bool | survival null | kappa null | tau null |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 0 | 1.983017 | 0.107975 | 2.028835 | 0.009612 | 2.019223 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 1 | 1.373383 | 0.092613 | 1.402322 | 0.037697 | 1.364626 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 2 | 0.983988 | 0.085458 | 1.010678 | 0.083168 | 0.92751 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 3 | 0.77409 | 0.090845 | 0.780398 | 0.144988 | 0.63541 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 4 | 0.663587 | 0.090522 | 0.66218 | 0.222167 | 0.440013 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 5 | 0.635649 | 0.089775 | 0.622872 | 0.31376 | 0.309112 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 6 | 0.656967 | 0.109498 | 0.640099 | 0.418867 | 0.221232 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 7 | 0.711532 | 0.120517 | 0.698684 | 0.536627 | 0.162057 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 8 | 0.797148 | 0.124283 | 0.788262 | 0.666221 | 0.122041 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 9 | 0.919189 | 0.123617 | 0.901689 | 0.806868 | 0.09482 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 10 | 1.040844 | 0.128031 | 1.033974 | 0.957824 | 0.07615 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 11 | 1.18882 | 0.144969 | 1.18158 | 1.118378 | 0.063201 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 12 | 1.351777 | 0.151142 | 1.341942 | 1.287856 | 0.054086 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 13 | 1.525043 | 0.153265 | 1.513157 | 1.465613 | 0.047545 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 200 | 14 | 1.703777 | 0.154357 | 1.693774 | 1.651036 | 0.042738 | 5 | 5 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 0 | 1.06944 | 0.101781 | 1.073964 | 0.036982 | 1.036982 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 1 | 0.678461 | 0.048564 | 0.694977 | 0.142294 | 0.552683 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 2 | 0.612743 | 0.052373 | 0.615999 | 0.308045 | 0.307954 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 3 | 0.710248 | 0.053307 | 0.71025 | 0.527046 | 0.183205 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 4 | 0.911774 | 0.063409 | 0.911374 | 0.792749 | 0.118625 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 5 | 1.204311 | 0.058571 | 1.183496 | 1.0992 | 0.084296 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 6 | 1.527638 | 0.066362 | 1.506234 | 1.440986 | 0.065247 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 7 | 1.888468 | 0.075745 | 1.867174 | 1.813194 | 0.05398 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 8 | 2.29956 | 0.087296 | 2.258101 | 2.211366 | 0.046735 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 9 | 2.701622 | 0.10733 | 2.673088 | 2.631465 | 0.041623 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 10 | 3.123308 | 0.117914 | 3.107528 | 3.069838 | 0.03769 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 11 | 3.579619 | 0.134566 | 3.557634 | 3.523184 | 0.03445 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 12 | 4.042824 | 0.115465 | 4.020179 | 3.988527 | 0.031652 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 13 | 4.505771 | 0.127834 | 4.492348 | 4.463183 | 0.029165 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 14 | 10 | 201 | 14 | 4.990538 | 0.110585 | 4.971657 | 4.944742 | 0.026915 | 2 | 2 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 0 | 0.792795 | 0.069736 | 0.800166 | 0.0801 | 0.720066 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 1 | 0.64053 | 0.083617 | 0.638072 | 0.30252 | 0.335552 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 2 | 0.844898 | 0.147941 | 0.819568 | 0.643049 | 0.176519 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 3 | 1.211874 | 0.155978 | 1.188878 | 1.080626 | 0.108252 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 4 | 1.730989 | 0.183079 | 1.673765 | 1.596961 | 0.076805 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 5 | 2.317232 | 0.215722 | 2.236747 | 2.176207 | 0.06054 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 6 | 2.927305 | 0.230305 | 2.855422 | 2.804668 | 0.050754 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 7 | 3.578019 | 0.253426 | 3.514459 | 3.470539 | 0.04392 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 8 | 4.236667 | 0.24007 | 4.20226 | 4.163672 | 0.038589 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 9 | 4.933228 | 0.229759 | 4.909523 | 4.875379 | 0.034144 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 10 | 5.641624 | 0.253296 | 5.628559 | 5.598251 | 0.030308 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 11 | 6.360849 | 0.28486 | 6.352937 | 6.325996 | 0.026942 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 12 | 7.114363 | 0.28057 | 7.077265 | 7.0533 | 0.023965 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 13 | 7.803153 | 0.286852 | 7.797029 | 7.775705 | 0.021324 | 1 | 1 | true | null | null | null |
A_fig1b | 25 | 2.5 | 1,200 | 480 | 1 | 1 | 0 | 14 | 10 | 202 | 14 | 8.491787 | 0.298706 | 8.508476 | 8.4895 | 0.018976 | 1 | 1 | true | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 0 | 1.484531 | 0.07527 | 1.5 | 0.25 | 1.25 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 1 | 1.25166 | 0.043733 | 1.25 | 0.5625 | 0.6875 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 2 | 1.134406 | 0.027185 | 1.125 | 0.765625 | 0.359375 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 3 | 1.059423 | 0.019492 | 1.0625 | 0.878906 | 0.183594 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 4 | 1.028117 | 0.01706 | 1.03125 | 0.938477 | 0.092773 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 5 | 1.016318 | 0.012275 | 1.015625 | 0.968994 | 0.046631 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 6 | 1.005745 | 0.007392 | 1.007813 | 0.984436 | 0.023376 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 7 | 1.003013 | 0.005037 | 1.003906 | 0.992203 | 0.011703 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 8 | 1.000212 | 0.003042 | 1.001953 | 0.996098 | 0.005856 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 9 | 0.999627 | 0.00173 | 1.000977 | 0.998048 | 0.002929 | 10 | null | false | null | null | null |
B_fig3a | 1 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 300 | 10 | 0.999272 | 0.001603 | 1.000488 | 0.999024 | 0.001465 | 10 | null | false | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 0 | 1.275594 | 0.063641 | 1.277778 | 0.138889 | 1.138889 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 1 | 1.128794 | 0.08509 | 1.132716 | 0.466821 | 0.665895 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 2 | 1.29007 | 0.129184 | 1.28738 | 0.887453 | 0.399927 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 3 | 1.601094 | 0.142739 | 1.586787 | 1.340309 | 0.246477 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 4 | 1.962901 | 0.111482 | 1.944292 | 1.788752 | 0.15554 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 5 | 2.310475 | 0.106492 | 2.312005 | 2.211804 | 0.100201 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 6 | 2.679607 | 0.088523 | 2.664294 | 2.598616 | 0.065678 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 7 | 3.015448 | 0.102182 | 2.988416 | 2.944759 | 0.043657 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 8 | 3.319222 | 0.090511 | 3.279079 | 3.249738 | 0.02934 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 9 | 3.573267 | 0.06822 | 3.535251 | 3.515364 | 0.019887 | 1 | null | true | null | null | null |
B_fig3a | 5 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 301 | 10 | 3.786819 | 0.065062 | 3.758256 | 3.74469 | 0.013566 | 1 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 0 | 1.083717 | 0.093107 | 1.073964 | 0.036982 | 1.036982 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 1 | 0.692784 | 0.076266 | 0.694977 | 0.142294 | 0.552683 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 2 | 0.621145 | 0.099438 | 0.615999 | 0.308045 | 0.307954 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 3 | 0.714474 | 0.135992 | 0.71025 | 0.527046 | 0.183205 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 4 | 0.905728 | 0.164817 | 0.911374 | 0.792749 | 0.118625 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 5 | 1.174443 | 0.205258 | 1.183496 | 1.0992 | 0.084296 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 6 | 1.501141 | 0.228941 | 1.506234 | 1.440986 | 0.065247 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 7 | 1.876899 | 0.261165 | 1.867174 | 1.813194 | 0.05398 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 8 | 2.274799 | 0.282286 | 2.258101 | 2.211366 | 0.046735 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 9 | 2.712666 | 0.322064 | 2.673088 | 2.631465 | 0.041623 | 2 | null | true | null | null | null |
B_fig3a | 25 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 302 | 10 | 3.133746 | 0.308466 | 3.107528 | 3.069838 | 0.03769 | 2 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 0 | 1.017064 | 0.103717 | 1.019606 | 0.009803 | 1.009803 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 1 | 0.546866 | 0.064885 | 0.553336 | 0.038825 | 0.514511 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 2 | 0.338107 | 0.047919 | 0.35317 | 0.086494 | 0.266676 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 3 | 0.271549 | 0.038926 | 0.294826 | 0.152253 | 0.142573 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 4 | 0.289864 | 0.040279 | 0.315897 | 0.235558 | 0.080339 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 5 | 0.357589 | 0.041974 | 0.38492 | 0.335875 | 0.049044 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 6 | 0.454698 | 0.056088 | 0.485908 | 0.452686 | 0.033222 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 7 | 0.573156 | 0.068968 | 0.610621 | 0.585482 | 0.025139 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 8 | 0.706397 | 0.07717 | 0.754696 | 0.733767 | 0.02093 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 9 | 0.860094 | 0.084654 | 0.915716 | 0.897056 | 0.01866 | 3 | null | true | null | null | null |
B_fig3a | 100 | 2 | 1,200 | 600 | 1 | 1 | 0 | 10 | 10 | 303 | 10 | 1.034218 | 0.088193 | 1.092241 | 1.074877 | 0.017364 | 3 | null | true | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 0 | 1.994066 | 0.165001 | 2.028835 | 0.009612 | 2.019223 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 1 | 1.391201 | 0.101401 | 1.402322 | 0.037697 | 1.364626 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 2 | 1.013913 | 0.076457 | 1.010678 | 0.083168 | 0.92751 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 3 | 0.794551 | 0.088788 | 0.780398 | 0.144988 | 0.63541 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 4 | 0.682499 | 0.093005 | 0.66218 | 0.222167 | 0.440013 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 5 | 0.646736 | 0.097038 | 0.622872 | 0.31376 | 0.309112 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 6 | 0.665967 | 0.109474 | 0.640099 | 0.418867 | 0.221232 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 7 | 0.728047 | 0.118597 | 0.698684 | 0.536627 | 0.162057 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 8 | 0.82183 | 0.133766 | 0.788262 | 0.666221 | 0.122041 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 9 | 0.937301 | 0.148067 | 0.901689 | 0.806868 | 0.09482 | null | null | null | null | null | null |
C_decomposition | 25 | 1.5 | 1,200 | 800 | 1 | 1 | 0 | 14 | 10 | 400 | 10 | 1.059508 | 0.15581 | 1.033974 | 0.957824 | 0.07615 | null | null | null | null | null | null |
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