# Claims Registry

Auto-generated by `scripts/populate_claims_registry.py` from files under
`results/`. Do not hand-edit the table -- it will be overwritten. Every
numerical claim that appears in the website or paper MUST have a row here.

| claim_id | statement | source result file | hash (12 char) | pipeline command |
|---|---|---|---|---|
| baseline_xi_r1p2 | xi(r=1.22 Mpc) = 14.153 +/- 3.230 | `results/baseline_lrg_sgc/xi_result.npz` | `ae478557d9ef` | `scripts/run_clustering_baseline.py` |
| baseline_xi_r127 | xi(r=127.4 Mpc) = 0.0032 +/- 0.0010 | `results/baseline_lrg_sgc/xi_result.npz` | `ae478557d9ef` | `scripts/run_clustering_baseline.py` |
| baseline_power_law_r0_full_1_30_mpc | r0 = 9.14 +/- 0.04 Mpc/h | `results/baseline_lrg_sgc/power_law_fit.json` | `ae478557d9ef` | `power-law fit script (inline, see decision_log.md)` |
| baseline_power_law_gamma_full_1_30_mpc | gamma = 1.545 +/- 0.009 | `results/baseline_lrg_sgc/power_law_fit.json` | `ae478557d9ef` | `power-law fit script (inline, see decision_log.md)` |
| baseline_power_law_r0_two_halo_3_30_mpc | r0 = 9.16 +/- 0.03 Mpc/h | `results/baseline_lrg_sgc/power_law_fit.json` | `ae478557d9ef` | `power-law fit script (inline, see decision_log.md)` |
| baseline_power_law_gamma_two_halo_3_30_mpc | gamma = 1.673 +/- 0.011 | `results/baseline_lrg_sgc/power_law_fit.json` | `ae478557d9ef` | `power-law fit script (inline, see decision_log.md)` |
| footprint_area | 3772 deg^2 (9.14% of sky) | `results/geometry_summary_lrg_sgc.json` | `N/A` | `scripts/make_footprint_figure.py` |
| n_lrg_objects | 662,492 LRG targets | `results/geometry_summary_lrg_sgc.json` | `N/A` | `scripts/make_footprint_figure.py` |
| injection_contaminated_gradient_boosting | [contaminated/gradient_boosting] contamination_corr=0.0273, rms_cl_distortion=0.0785 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_contaminated_linear | [contaminated/linear] contamination_corr=-0.0030, rms_cl_distortion=0.0997 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_contaminated_no_correction | [contaminated/no_correction] contamination_corr=0.5998, rms_cl_distortion=1.9134 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_contaminated_random_forest_deep | [contaminated/random_forest_deep] contamination_corr=0.0058, rms_cl_distortion=0.0902 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_contaminated_random_forest_shallow | [contaminated/random_forest_shallow] contamination_corr=0.0847, rms_cl_distortion=0.1585 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_negative_control_gradient_boosting | [negative_control/gradient_boosting] contamination_corr=0.0003, rms_cl_distortion=0.0565 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_negative_control_linear | [negative_control/linear] contamination_corr=0.0014, rms_cl_distortion=0.0507 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_negative_control_no_correction | [negative_control/no_correction] contamination_corr=0.0097, rms_cl_distortion=0.0000 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_negative_control_random_forest_deep | [negative_control/random_forest_deep] contamination_corr=-0.0038, rms_cl_distortion=0.0555 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |
| injection_negative_control_random_forest_shallow | [negative_control/random_forest_shallow] contamination_corr=0.0013, rms_cl_distortion=0.0492 | `results/injection_study/injection_study_summary.json` | `ae478557d9ef` | `scripts/run_injection_study.py` |