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Paper Citation Record · LEDGER

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning

As of 12 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2608.08197.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.08197 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:20:53.114122Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

  • verified exact1
  • verified fuzzy9
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27afd890-250a-423d-bdfb-0e162120d5e2 · outbound

This paper cites Sarawagi, R.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Sarawagi, R

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.362850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.047078Z digest=sha256:d25c3e7424371b24022ac9ed0a1e007d720481b486ec61deda80396b285ef245

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.349521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.052259Z digest=sha256:9e5ba83436b5c615e5bb92861de31b946d3c6d4b3ce2937c60dc703078f06c86

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.334861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.057109Z digest=sha256:54975125f4739df63c90c78b3916c9278519c39e9b850770ec3085233a402be8

Observation 703d5e39-fa09-466b-8eff-cbaee91e4092 · outbound

This paper cites Joglekar, H.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Joglekar, H

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.320636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.061729Z digest=sha256:dd0df79051c06017127c7b5d1ca9bc7964b322c1944a07085f079652f0feb1dd

Observation 5ea88ed3-3e77-4ad9-884d-07577d5928fa · outbound

This paper cites an unresolved cited work.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-12T00:20:53.305538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.066537Z digest=sha256:4f63480cdc2d410aea6a06c05074bdc33b933162a60f577178d2bc0eee121c63

Observation 73c8e9dd-9f7b-4779-9d49-a63e45713645 · outbound

This paper cites Duivesteijn, A.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Duivesteijn, A

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.290714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.070891Z digest=sha256:43b9a1a41361a4ae7e79576293cc4cccca1daa830bcdf2011233b7f7dbcd45eb

Observation 93f855cf-f10e-41de-852e-2c9eba556b8b · outbound

This paper cites Lemmerich, M.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Lemmerich, M

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.277627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.076222Z digest=sha256:78b9e7a8303f40e80df9fecb5f2f43cd268ccc1cc4ba31ffa9aa4cc9c5a70029

Observation faf5f0f1-9a53-42d4-9882-583c51e6e56c · outbound

This paper cites Moshkovitz, S.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Moshkovitz, S

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.262860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.082032Z digest=sha256:0d9946954e3bab69838f54cda6f221dd7a0b2104fa408a7cda90d040eb6e5c54

Observation bc50bfe3-893e-40ab-a754-72419a01ebcc · outbound

This paper cites Near-optimal Algorithms for Explainable k-Medians and k-Means.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Near-optimal Algorithms for Explainable k-Medians and k-Means

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-12T00:20:53.177445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.088142Z digest=sha256:f1016ad79e1185499f78ac049ac730248418cca5adbb2b823c382f93d42bd0d9

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-12T00:20:53.092590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:20:53.092590Z digest=sha256:4fdf01c9c281457199c919d7daa237fcdbf216432cafae9082cff50802ec50aa

Observation dec8719a-213f-4665-9f9c-8f0a5ce99f0a · outbound

This paper cites Aunified approachtointerpretingmodelpredictions.InAdvances in Neural Information Processing Systems (NIPS), volume 30, pages 4765–4774, 2017.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Aunified approachtointerpretingmodelpredictions.InAdvances in Neural Information Processing Systems (NIPS), volume 30, pages 4765–4774, 2017

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.248217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.097111Z digest=sha256:d68fa7e7ba006c9f79c7373ea323395d4dab4b2a66d755eb6a037e846fb44ea2

Observation b1876a1f-b3d8-4086-b063-85e2fade3c61 · outbound

This paper cites an unresolved cited work.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Unresolved cited work

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-12T00:20:53.101646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:20:53.101646Z digest=sha256:b9498b17c90b0e2094597b5c7364fb345e282ac4bd6fe8b40cea2010d4148355

Observation 1ff89307-e69d-41cf-90bd-1e708f55ec3a · outbound

This paper cites an unresolved cited work.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-12T00:20:53.223404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.105990Z digest=sha256:8eca81c54048cd8dd26e744ae80d197de4882d347274b6fb59fb21e0e992ea30

Observation 38cd509f-2996-454d-a735-8246d427faab · outbound

This paper cites Pedregosa, G.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Pedregosa, G

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T00:20:53.207044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.110216Z digest=sha256:0f777126c618f63bade318fa43266eed8c57031867affed4b36b3d85426e11f6

Observation e38e2f25-bb22-46b1-ad03-2fbd73b48c15 · outbound

This paper cites an unresolved cited work.

Self-Explaining Segment Trees: A KPI-Conditioned Segmentation Framework for Business Analytics with Node-Level Explanation via Recursive Subspace Partitioning Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-12T00:20:53.192957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T00:20:53.114122Z digest=sha256:df3abdc270794458d53e0a6e19731e4a33bc1829db129b28b888b72f8535c02a

Pith citing papers

No inbound Pith citation observations are available.