Region Discovery

Region Discovery answers a section-level question: did the predictor recover the right coding runs, independent of fine-grained boundary residuals?

How Sections Are Matched

The benchmark first extracts contiguous GT and predicted coding sections. It then finds all overlapping (GT, pred) pairs and sorts them by overlap length. A greedy 1:1 assignment claims the largest overlaps first.

perfect_boundary_hit is different: it uses a sweep over all sections and counts any exact-boundary match, without the 1:1 assignment.

Output Structure

Region Discovery produces four precision/recall tiers that nest by strictness:

neighborhood_hit  ⊇  { internal_hit , full_coverage_hit }  ⊇  perfect_boundary_hit

Tier	TP criterion	Question answered
`neighborhood_hit`	matched pair overlaps at all	Did we detect the region?
`internal_hit`	prediction lies within GT (`pred ⊆ GT`)	Detected, but under-extended?
`full_coverage_hit`	prediction spans GT (`pred ⊇ GT`)	Detected, but over-extended?
`perfect_boundary_hit`	both boundaries exact	Did we reproduce exact boundaries?

Every tier is a coherent contingency table: TP + FN = total GT and TP + FP = total pred. internal_hit and full_coverage_hit are inclusive of an exact match, so a perfect pair counts toward both — that is what makes the nesting hold.

False positives — the same rule for every tier

Each tier books its FP as total_pred − that-tier's hits. A matched pair that fails a tier’s spatial test counts as both an FP (the prediction is not a hit for this tier) and an FN (the GT is not hit for this tier). This is the standard Burset/Eval exon-level behavior, and it is what makes precision and recall well-defined at every level of strictness — the whole family reads as one precision/recall ladder.

The Four P/R Tiers

`neighborhood_hit`

TP if the matched prediction overlaps the GT section at all. The most forgiving detection tier — any contact counts.

`internal_hit`

TP if the matched prediction lies within its GT section (pred ⊆ GT, inclusive of an exact match) — i.e. the prediction did not over-run the GT boundaries. A matched pair that over-extends past the GT is an FP (and its GT an FN).

`full_coverage_hit`

TP if the matched prediction fully spans its GT section (pred ⊇ GT, inclusive of an exact match) — i.e. the prediction did not fall short of the GT boundaries. A matched pair that falls short is an FP (and its GT an FN).

internal_hit and full_coverage_hit give the direction of the boundary error: contrast their recalls to read under- vs over-extension.

`perfect_boundary_hit`

TP only when both boundaries match exactly. Unlike the other tiers this is sweep-based rather than 1:1 matched, which prevents fragmented predictions from being miscounted purely because one fragment already claimed a GT section in the greedy assignment.

Double-Penalty Behavior

All tiers intentionally treat GT sections and prediction sections as separate objects. When one GT section is split into two predictions, or two GT sections are merged into one prediction, you often get both a false negative on the GT side and a false positive on the prediction side. That is the right behavior if you want structural section recovery rather than base-level overlap alone.

Interpretation

high neighborhood_hit recall, low perfect_boundary_hit recall: the model usually finds the right locus but misses exact boundaries
high internal_hit recall, low full_coverage_hit recall: predictions tend to be too short (under-extended)
low internal_hit recall, high full_coverage_hit recall: predictions tend to be too long (over-extended)
both internal_hit and full_coverage_hit recall near neighborhood_hit: matched predictions are close to exact even when perfect_boundary_hit is lower (a fragmentation effect)

Caveats

These metrics are coding-section metrics. They do not use intron labels.
They are not transcript-chain metrics. Two transcripts can have good section discovery while still failing strict structural coherence.
perfect_boundary_hit TP/FP/FN counts come from the sweep, so absolute counts are not directly comparable to the 1:1-matched tiers.
Aggregation is micro-averaged across sequences (see Conventions): per-sequence integer counts are summed before ratios are computed, so long sequences dominate the corpus score.