The Earth-shadow deficit becomes stronger at all tested altitudes after applying the quality filtering.

In the group's 2026 modeling study, Table 1 explicitly shows that applying machine-learning quality filtering (p > 0.7) increases the calculated Earth-shadow deficit across all assumed orbital altitudes compared to the complete working sample. This confirms the mathematical behavior of the catalog under the authors' filtering protocol. The strongest opposing consideration is that the ML classifier was trained on subjective visual labels rather than confirmed physical ground truth, and the filtered shadow count drops to as few as 17 objects, introducing small-number statistics.

TRN-E052/TRN-E053 document the strengthening under the group's own ML filter, including 0.805 ± 0.049 at 35,000 km. But the filter and the catalog originate with the same group, a classifier trained on visually labeled imagery would amplify any signal that defects dilute whether or not the residual is astrophysical, and only ~17 filtered transients fall in shadow at the higher altitudes. Strongest opposing consideration: the result is fully predicted by defect-dilution dynamics inside a contested catalog and is not independent of TRN-E015.

TRN-E052 and TRN-E053 directly report that the quality-filtered sample has a stronger deficit at every tested altitude, including its maximum at 35,000 km. This is consistent with the earlier ML-stratified result in TRN-E015. The strongest opposing consideration is that both the filter and catalog come from the same lineage, labels lack physical ground truth, and only 17 filtered objects occupy the shadow at higher altitudes.

The modelling preprint reports that after the group’s own p>0.7 quality filter the Earth-shadow deficit becomes stronger at all tested altitudes, largest near 35,000 km. The result is internal robustness within one contested lineage and one ML filter trained on expert visual labels, not external validation. Strongest opposing consideration: a filter that removes defect-like rows will inflate any residual signal whether or not that residual is astrophysical.

TRN-E052 and TRN-E053 report the claim almost verbatim from the source, so as a description of what the quality-filtered analysis returns it is accurate. Its evidential value is another matter: the filter is the same group's ML classifier trained on their own visual labels (TRN-E015), and any filter that preferentially removes defect-like rows will amplify whatever signal defects dilute, whether or not the residual is astrophysical. The strongest opposing consideration is the small-number regime the record itself flags — only 17 quality-filtered transients fall in shadow at altitudes ≥35,000 km, where the largest deficit (0.805 ± 0.049) is reported.

TRN-E052 and TRN-E053 (moderate): after the p>0.7 ML filter the deficit is larger at every assumed altitude, peaking at 35,000 km (0.805 ± 0.049). Both records note the filter is the group's own classifier trained on visual labels (TRN-E015), only 17 filtered transients fall in shadow at ≥35,000 km, and strengthening under a defect-removing classifier is expected for any signal defects dilute, whether or not the residual is the sky.

TRN-E052 and TRN-E053 (moderate, supports) report the strengthening at all tested altitudes and its peak at 35,000 km on the quality-filtered sample. Both come from the same preprint, use the group's own classifier, and reach a small-number regime (17 in shadow at ≥35,000 km).

Reanalysis of the VASCO dataset demonstrates that applying machine-learning quality filtering (p > 0.7) increases the observed shadow deficit across all tested altitudes, reaching a peak deficit of over 80% at 35,000 km. While this confirms the mathematical behavior of the filtered catalog, it remains internal to the team's proprietary classifier. Strongest opposing consideration: The filtered sample contains very few events in shadow (only 17 at high altitudes), making the heightened statistical significance sensitive to small-number fluctuations and selection bias.

TRN-E052/E053 directly report the strengthened post-filter deficit, but the filter is the same group's ML classifier trained on visually labeled imagery: removing defect-like rows amplifies any signal that defects dilute, whether or not the residual is astrophysical, and only 17 quality-filtered transients fall in shadow at ≥35,000 km. Strongest opposing consideration: the result is neither independent of TRN-E015 nor robust in the small-number regime.

TRN-E052 states that quality filtering strengthens the Earth-shadow deficit at every tested altitude, and TRN-E053 gives the largest filtered deficit at 35,000 km. These records directly support the claim as an internal result of the group's analysis. The strongest opposing consideration is that both the filter and catalog come from the same lineage, the labels lack physical ground truth, and some filtered in-shadow comparisons contain only 17 objects.

The modelling preprint states that after the group’s own p>0.7 quality filter the shadow deficit becomes stronger at all tested altitudes, reaching D≈0.80 at 35 000 km (E052, E053). The filter and catalog are internal; small-number statistics apply in the shadow. Strongest opposing consideration is that a classifier trained to remove defect-like rows will necessarily inflate any residual signal whether or not that residual is astrophysical.

The group's Table 1 reports this, consistent with E015 (E052, E053). But the "quality filter" bundles ML probability (p>0.7) with RA/Dec cuts. The strengthening therefore cannot be credited to defect removal rather than spatial selection. Only 17 filtered transients fall in shadow at ≥35,000 km. Strongest opposing consideration: the result is unreproduced. A filter trained on expert labels of the same contested imagery would sharpen any structure that defects dilute, astrophysical or not.

TRN-E052 and TRN-E053 report the deficit larger at every assumed altitude after filtering to p>0.7, reaching 0.805 at 35,000 km. The source supports the claim as stated. Its meaning is limited: the filter and the catalog are the same group's, and few filtered candidates fall in shadow at high altitudes.