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Paper Citation Record · LEDGER

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems

As of 22 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2509.01972.

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

pith.paper-citation-record.v1
2509.01972 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T12:05:49.954123Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

26 of 26 outbound references displayed

  • verified exact2
  • verified fuzzy11
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 421e8bea-72cd-4c6d-9da5-f91eeae6326c · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-05T12:05:50.399661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 87d8779d-287b-4ec6-803c-dc739ef17567 · outbound

This paper cites Transfer learning was more effective for the resolution coarsening scenarios, where model structures remained the same or similar but spatial heterogeneity was high.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Transfer learning was more effective for the resolution coarsening scenarios, where model structures remained the same or similar but spatial heterogeneity was high

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T12:05:50.389249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 304d601f-9b8b-45e8-b301-eee161cd74bd · outbound

This paper cites aggregated.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems aggregated

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation c630d298-b171-4398-9811-0cf66423c06c · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 4

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 92a9cafb-6056-4ce4-a88a-4b8733dafd40 · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 5

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ae70fcb1-b462-4499-aaed-7a4b88ab02ac · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 6

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation f943efba-5dd5-4e0a-93ea-9ed8675a66ab · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-05T12:05:50.337325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 1dff6400-bc2f-495d-9aa8-da84197b501b · outbound

This paper cites 5(c)): Despite using a different nitrification equation, the model reproduce d outputs with good accuracy, demonstrating transferability between formulations in EcoHydroModel.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems 5(c)): Despite using a different nitrification equation, the model reproduce d outputs with good accuracy, demonstrating transferability between formulations in EcoHydroModel

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T12:05:50.327308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 299cfac3-b4c1-42f3-ae44-332cd14189bc · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 9

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 3e7c75de-3aae-421c-bf2f-8e543b45e561 · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-05T12:05:50.306983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 00db6e94-b73e-4464-a6b1-0d788434253b · outbound

This paper cites This limits their ability to configure models, identify dominant processes, or implement structural changes.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems This limits their ability to configure models, identify dominant processes, or implement structural changes

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 52fc7454-f167-4971-8077-887eef934c62 · outbound

This paper cites This complexity manifests as unstructured data and code architectures, hindering AI from constructing coherent, structured representations.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems This complexity manifests as unstructured data and code architectures, hindering AI from constructing coherent, structured representations

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T12:05:50.286254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 153a4df2-6dec-4e53-a9d4-f0d1c7b515cb · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 13

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 43405863-fd2b-44ea-8429-17efcb91da47 · outbound

This paper cites Without domain -specific priors, AI models often struggle to transfer knowledge across watersheds or adapt to unfamiliar simulation settings.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Without domain -specific priors, AI models often struggle to transfer knowledge across watersheds or adapt to unfamiliar simulation settings

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T12:05:50.265660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation da43693d-761c-4707-b2b4-f7e08c03cbd6 · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-05T12:05:50.255385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation dca86ac0-2263-426a-a6ee-b2eee190549f · outbound

This paper cites Brain (B): Knowledge Integration and Human-like Reasoning.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Brain (B): Knowledge Integration and Human-like Reasoning

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T12:05:50.244912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 16a1bf01-8f9f-4669-b85d-2bfcca87ded6 · outbound

This paper cites Instruction tuning aligns this knowledge with domain semantics for targeted reasoning.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Instruction tuning aligns this knowledge with domain semantics for targeted reasoning

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T12:05:50.234240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 30f6f476-07b2-4e21-a5a6-b2461a9ed37f · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-05T12:05:50.222724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 0b512ad7-3859-42ee-a3d2-f3980008181f · outbound

This paper cites Hands (H): Model Construction, Execution, and Validation.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Hands (H): Model Construction, Execution, and Validation

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 88ee7bd8-7c92-4c29-b26a-56641584082a · outbound

This paper cites They begin with the definition of conceptual structures and the assembly or selection of model components.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems They begin with the definition of conceptual structures and the assembly or selection of model components

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation cbaecbc0-7029-4b4f-b6fa-92a87e34e7ed · outbound

This paper cites Integration of knowledge graphs and causal reasoning enables evaluation of ecohydrological impacts and supports multi-objective optimization.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Integration of knowledge graphs and causal reasoning enables evaluation of ecohydrological impacts and supports multi-objective optimization

Reference 21

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 6dc17086-5df6-487b-acb7-48fee3397e21 · outbound

This paper cites an unresolved cited work.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Unresolved cited work

Reference 22

Resolution
unresolved
raw_fallback, observed 2026-08-05T12:05:50.177038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation b39d47a9-67dd-4c4d-add4-3bb758c96f38 · outbound

This paper cites overparameterization.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems overparameterization

Reference 23

Resolution
verified exact
doi, observed 2026-08-05T12:05:50.093586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 27ee764b-8393-4c31-98be-13dc6a5ecc43 · outbound

This paper cites Stochastic Training is Not Necessary for Generalization.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems Stochastic Training is Not Necessary for Generalization

Reference 60

Resolution
metadata mismatch
local_arxiv, observed 2026-08-05T12:05:50.000752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-05T12:05:49.947002Z digest=sha256:efe5d4d997e6e2e2e4ad0d201a4fd7d7a2baaf2b0517bb09e3723c52c51ad2f1

Observation 150e8808-9e1f-4fec-952e-f2bfe61a1039 · outbound

This paper cites high-flow.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems high-flow

Reference 2331

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:05:49.954123Z digest=sha256:a31e3596018a154081dbe585dadff67106b135085ad4ff9ed6635871f3ecee94

Observation f1771c6e-d55b-47d4-8a80-5d3138088ac7 · outbound

This paper cites H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V.

Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V

Reference 7555

Resolution
verified exact
doi, observed 2026-08-05T12:05:49.986877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Pith citing papers

No inbound Pith citation observations are available.