{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp04_retrieval_first_paired20_20260802","cell_id":"invariant_mode_decomposition_design__library_information_science","hypothesis_id":"H4","search_queries":["interlibrary loan routing algorithm centrality eigenvector lender selection","interlibrary loan load balancing lender routing resource sharing network","patent interlibrary loan routing library request lender selection","ISO 18626 interlibrary loan routing supplier selection","site:help.oclc.org WorldShare ILL automated request manager lender string load balancing","library resource sharing system algorithm distribute requests evenly lenders","interlibrary loan network robustness node removal centrality","\"interlibrary loan\" \"eigenvector centrality\""],"sources":[{"source_id":"PA1","title":"Smart lender strings through the Automated Request Manager","publisher":"OCLC Support","url":"https://help.oclc.org/Resource_Sharing/WorldShare_Interlibrary_Loan/Smart_fulfillment/040Smart_lender_strings_through_the_Automated_Request_Manager","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["WorldShare ILL already automates lender-string construction and request assignment.","It ranks eligible lenders using historical turnaround time and current queue depth specifically to avoid overwhelming fast lenders.","It therefore establishes load-aware routing with fulfillment-speed protection, but the documented criteria are not spectral centrality or simulated node-loss sensitivity."]},{"source_id":"PA2","title":"Load balancing algorithm","publisher":"ReShare / Open Library Foundation","url":"https://openlibraryfoundation.atlassian.net/wiki/spaces/PR/pages/2551775240/Load%2Bbalancing%2Balgorithm","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["ReShare uses load-balancing scores to order suppliers for each request.","Scores are dynamically recalculated from active requests and each library's target loan-to-borrow ratio.","This is a deployed domain-specific analogue for diverting work from overburdened lenders, but it uses reciprocal-volume ratios rather than network dependency or removal sensitivity."]},{"source_id":"PA3","title":"Chapter 1: Introduction to Resource Sharing","publisher":"American Library Association, Library Technology Reports","url":"https://journals.ala.org/index.php/ltr/article/view/4407/5105","source_class":"AUTHORITATIVE_SECONDARY","claims_supported":["Even distribution of resource-sharing workload and avoidance of disproportionate lender burden are established professional objectives.","The report states that resource-sharing business logic should select potential lenders in ways that balance load.","It also identifies automated routing, transaction standards, and supplier-chain tracking as established infrastructure."]},{"source_id":"PA4","title":"How does DOCLINE routing work?","publisher":"U.S. National Library of Medicine","url":"https://www.nlm.nih.gov/docline/context_help/context_help_routing_choices.html","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["DOCLINE automatically routes eligible ILL requests after filtering for holdings, format, service, participation, and other constraints.","Within routing-table cells and resource-library groups, documented selection is random, furnishing a directly relevant diversification baseline.","The documented algorithm does not use eigenvector centrality, counterfactual node removal, or system-wide dependency scores."]},{"source_id":"PA5","title":"Robustness envelopes of networks","publisher":"Journal of Complex Networks, Oxford University Press","url":"https://academic.oup.com/comnet/article/1/1/44/509398","source_class":"PRIMARY_RESEARCH","claims_supported":["Network robustness assessment through simulated random and centrality-targeted node removal is established research practice.","The study finds that centrality-guided removals indicate worst-case network behavior.","It specifically reports that degree and eigenvector centrality together may suffice to evaluate worst-case behavior, overlapping the hypothesis's dominant-mode and node-loss logic outside the ILL domain."]}],"proximity":"ADJACENT_PRIOR_ART","closest_analogues":[{"name":"OCLC WorldShare smart lender strings","similarity":"High: an operational ILL system ranks eligible lenders per request using queue depth and turnaround history to prevent overload while preserving speed.","remaining_difference":"No documented request-flow eigenvector score, simulated-removal sensitivity threshold, or resilience objective.","source_ids":["PA1"]},{"name":"ReShare ratio-based load balancing","similarity":"High: supplier order is dynamically changed to distribute ILL work away from libraries exceeding a target load.","remaining_difference":"The score reflects active loan-to-borrow balance, not structural dependency in a rolling flow network.","source_ids":["PA2"]},{"name":"Centrality-targeted network robustness analysis","similarity":"High at the mechanism level: eigenvector centrality and node-removal simulation are established ways to identify network vulnerability.","remaining_difference":"The research does not apply the analysis to ILL transactions or convert it into an eligible-request routing policy with fulfillment-time constraints.","source_ids":["PA5"]},{"name":"DOCLINE random eligible-lender routing","similarity":"Moderate: requests are diversified among eligible lenders rather than deterministically concentrated on one supplier.","remaining_difference":"Diversification is random within configured groups and is not conditioned on load, dominant modes, or removal consequences.","source_ids":["PA4"]}],"overlapping_components":["Eligible-lender candidate filtering","Automated request-by-request lender ordering","Dynamic lender-load balancing","Avoidance of disproportionate lender burden","Turnaround-time-aware routing","Randomized lender diversification","Centrality-based identification of critical nodes","Eigenvector-centrality analysis","Simulated node-removal robustness testing"],"remaining_contrastive_claim":"Within an interlibrary-loan consortium, adding a removal-sensitivity-gated eigenvector-dependency penalty to eligible-lender routing will improve node-loss resilience and maximum lender-load share beyond queue-depth, reciprocal-ratio, volume, degree, and random-routing baselines without materially increasing median fulfillment time.","claim_falsifier":"The claim would be falsified by an earlier accessible product, patent, standard, or study implementing that same spectral-removal-sensitive ILL routing combination, or by historical-log replay showing no statistically and operationally meaningful resilience/load advantage over the stated baselines at the fulfillment-time constraint.","problem_support":"STRONG","recommendation":"RESEARCH","world_novelty_boundary":"This bounded eight-query ordinary-web search found established ILL load-aware routing and established eigenvector/node-removal robustness analysis, but no opened source combining them as a removal-sensitive spectral ILL routing rule; proprietary ranking logic, unindexed patents, non-English literature, and inaccessible full texts remain outside the demonstrated boundary."}