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  "slug": "catenary-placement-classification",
  "title": "A placement criterion for catenary compartment models",
  "shortTitle": "Where to put the sensors",
  "url": "https://evidencepress.org/releases/catenary-placement-classification/",
  "oneLine": "An ordered placement rule classifies generic local identifiability in bidirected chains with arbitrary inputs, outputs and leaks.",
  "abstract": "This unrefereed candidate gives a necessary-and-sufficient placement criterion for generic local structural identifiability of every positive labelled bidirected-path compartment model with independently unknown transfer rates and prescribed leaks. The full labelled transfer matrix and known input/output gains are assumed. Interval ranks and ordered recurrences classify arbitrary input, output and leak sets; an augmented output count handles a dependence that ordinary count addition misses. The package contains the written proof, finite replay, review responses and an explicit assurance boundary.",
  "datePublished": "2026-09-23",
  "dateModified": "2026-09-23",
  "version": "0.1.0-candidate",
  "doi": "10.5281/zenodo.22909460",
  "doiUrl": "https://doi.org/10.5281/zenodo.22909460",
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  "authors": [
    "Anonymous"
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      "note": "Internal model reports and supplied evidence do not establish this external assurance dimension."
    },
    {
      "dimension": "formalVerification",
      "label": "Formal verification",
      "question": "Is a formalised statement machine-checked, and over which trusted base?",
      "state": "not-assessed",
      "note": "Internal model reports and supplied evidence do not establish this external assurance dimension."
    },
    {
      "dimension": "specialistReview",
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      "question": "Has a domain specialist assessed the argument?",
      "state": "not-assessed",
      "note": "Internal model reports and supplied evidence do not establish this external assurance dimension."
    },
    {
      "dimension": "editorialPeerReview",
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      "question": "Has a journal or venue run peer review to a decision?",
      "state": "not-assessed",
      "note": "Internal model reports and supplied evidence do not establish this external assurance dimension."
    },
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      "question": "Are data and computational environment pinned well enough to rebuild?",
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    "disclosure": "AI-assisted proof development, supplied-review response and internal model editorial review. Separately constructed code and model roles do not establish unaffiliated external validation."
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  "problem": {
    "name": "Ahmed et al. (2026), Section 6: multiple-input/output catenary classification",
    "url": "https://doi.org/10.1137/25M1728636"
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  "keywords": [
    "structural identifiability",
    "compartment models",
    "bidirected paths",
    "catenary models",
    "Jacobi matrices",
    "sensor placement"
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  "keyResults": [
    "Candidate necessary-and-sufficient classification for arbitrary prescribed port and leak placements at every path length.",
    "Interval source ranks separate source ambiguity from spectral ambiguity.",
    "A distinct augmented recurrence is necessary when the first leak is at or beyond the first output.",
    "A seven-compartment example shows why replacing the augmented count by the ordinary count plus one fails."
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        "title": "A placement criterion for catenary compartment models"
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        "title": "Internal five-role editorial assessment",
        "date": "2026-09-23",
        "recommendation": "Accept"
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        "publicUrl": "https://github.com/ipitchford/catenary-placement-classification/blob/v0.1.0-candidate/reviews/EDITORIAL_DECISION.md",
        "publicSummary": "Three roles recommended acceptance and two minor revision on one frozen target. The editor verified notation and documentation repairs and accepted the candidate with explicit limits. Original target and post-review differences are retained."
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  "openProblems": [
    "Obtain unaffiliated specialist scrutiny and reproduction of the arbitrary-placement proof.",
    "Determine global identifiability degrees and characterize the remaining finite ambiguity.",
    "Study robust experiment and sensor design under noise, limited excitation and uncertain gains.",
    "Extend beyond labelled paths while preserving the prescribed physical parameter dependencies."
  ],
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  "relatedWorks": [
    {
      "citation": "Ahmed et al. (2026), Identifiability of directed-cycle and catenary linear compartmental models: source classification problem.",
      "url": "https://doi.org/10.1137/25M1728636",
      "doi": "10.1137/25M1728636"
    },
    {
      "citation": "Bortner et al. (2023), Identifiability of linear compartmental tree models and a general formula for input-output equations: singleton-port predecessor.",
      "url": "https://doi.org/10.1016/j.aam.2023.102490",
      "doi": "10.1016/j.aam.2023.102490"
    },
    {
      "citation": "Bortner and Meshkat (2022), Identifiable paths and cycles in linear compartmental models: related path/cycle machinery.",
      "url": "https://doi.org/10.1007/s11538-022-01007-5",
      "doi": "10.1007/s11538-022-01007-5"
    },
    {
      "citation": "Ovchinnikov, Pogudin and Thompson (2023), Input-output equations and identifiability of linear ODE models: transfer-data interpretation.",
      "url": "https://doi.org/10.1109/TAC.2022.3145571",
      "doi": "10.1109/TAC.2022.3145571"
    },
    {
      "citation": "Boukhobza, Hamelin and Simon (2014), A graph theoretical approach to the parameters identifiability characterisation: free-entry model comparison.",
      "url": "https://doi.org/10.1080/00207179.2013.856519",
      "doi": "10.1080/00207179.2013.856519"
    }
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