#!/usr/bin/env python3 """Curated MDPI density-marker miner. This is a conservative metadata miner. It records paper-level route candidates and density markers, not article bodies. Each candidate is treated as an RRC prior until local replay/byte-law evidence exists. """ from __future__ import annotations import hashlib import json from pathlib import Path from typing import Any REPO = Path(__file__).resolve().parents[2] OUT_DIR = REPO / "shared-data" / "data" / "mdpi_density_markers" JSONL = OUT_DIR / "mdpi_density_marker_candidates.jsonl" CSV = OUT_DIR / "mdpi_density_marker_candidates.csv" RECEIPT = OUT_DIR / "mdpi_density_marker_miner_receipt.json" def stable_json(obj: Any) -> str: return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) def sha256_text(text: str) -> str: return hashlib.sha256(text.encode("utf-8")).hexdigest() CANDIDATES: list[dict[str, Any]] = [ { "candidate_id": "MDPI.MILPE.EIGENVECTOR_PROJECTION.2026.0001", "title": "Multivariate Identification via Linear Projection of Eigenvectors", "journal": "Mathematics", "year": 2026, "url": "https://www.mdpi.com/2227-7390/14/5/897", "doi": "10.3390/math14050897", "density_markers": [ "joint_input_output_solution_space", "cross_correlation_eigenvectors", "partial_eigenvector_replay", "low_rank_governing_equation_projection", ], "rrc_use": "LanguageSetMILPEProjection and density-marker eigenvector search", "claim_boundary": "algorithmic prior for projection/replay; not proof of language model correctness", "status": "CANDIDATE", }, { "candidate_id": "MDPI.ENTROPY.EIGENVECTOR_LOCALIZATION.2019.0001", "title": "Information Entropy of Tight-Binding Random Networks with Losses and Gain", "journal": "Entropy", "year": 2019, "url": "https://www.mdpi.com/1099-4300/21/1/86", "doi": "10.3390/e21010086", "density_markers": [ "eigenvector_information_entropy", "localized_extended_transition", "complex_spectrum_network_prior", "graph_adjacency_entropy_surface", ], "rrc_use": "score whether density-marker eigenvectors are localized, extended, or transition-like", "claim_boundary": "network spectral prior only until reproduced on local language/code graphs", "status": "HOLD", }, { "candidate_id": "MDPI.ENTROPY.AUTOENCODER_INFORMATION_FLOW.2021.0001", "title": "Information Flows of Diverse Autoencoders", "journal": "Entropy", "year": 2021, "url": "https://www.mdpi.com/1099-4300/23/7/862", "doi": "10.3390/e23070862", "density_markers": [ "information_plane_flow", "hidden_representation_compression_phase", "renyi_matrix_entropy", "sparsity_simplifying_phase", ], "rrc_use": "compare density-marker compression flow against hidden-representation simplification", "claim_boundary": "deep-learning diagnostic prior only; not a language compression result", "status": "HOLD", }, { "candidate_id": "MDPI.SENSORS.GRAPH_LIGHT_FIELD_CODING.2022.0001", "title": "Novel Projection Schemes for Graph-Based Light Field Coding", "journal": "Sensors", "year": 2022, "url": "https://www.mdpi.com/1424-8220/22/13/4948", "doi": "10.3390/s22134948", "density_markers": [ "graph_based_signal_redundancy", "irregular_shape_energy_compaction", "super_ray_projection", "graph_transform_coding", ], "rrc_use": "graph-transform analogy for irregular language/code density surfaces", "claim_boundary": "image/light-field coding prior only; needs local graph replay", "status": "HOLD", }, { "candidate_id": "MDPI.ENTROPY.COMPLEX_NETWORK_ENTROPY_SURVEY.2020.0001", "title": "A Survey of Information Entropy Metrics for Complex Networks", "journal": "Entropy", "year": 2020, "url": "https://www.mdpi.com/1099-4300/22/12/1417", "doi": "10.3390/e22121417", "density_markers": [ "graph_entropy_metric_catalog", "eigenvector_centrality_entropy", "topological_potential_entropy", "network_probability_distribution_choice", ], "rrc_use": "choose entropy measures for language-set density-marker graphs", "claim_boundary": "metric catalog prior; metric choice must be receipted per graph", "status": "CANDIDATE", }, { "candidate_id": "MDPI.ALGORITHMS.MAXENT_GRAPH_SPECTRUM.2022.0001", "title": "Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications", "journal": "Algorithms", "year": 2022, "url": "https://www.mdpi.com/1999-4893/15/6/209", "doi": "10.3390/a15060209", "density_markers": [ "maximum_entropy_spectral_density", "graph_moment_information", "massive_graph_spectrum_approximation", "kernel_free_spectral_estimate", ], "rrc_use": "estimate spectrum of large language/code manifold graphs without full eigendecomposition", "claim_boundary": "spectral approximation prior; not accepted until compared with local exact small graphs", "status": "HOLD", }, { "candidate_id": "MDPI.ENTROPY.NETWORK_CODING_THERMODYNAMICS.2019.0001", "title": "The Thermodynamics of Network Coding, and an Algorithmic Refinement of the Principle of Maximum Entropy", "journal": "Entropy", "year": 2019, "url": "https://www.mdpi.com/1099-4300/21/6/560", "doi": "10.3390/e21060560", "density_markers": [ "algorithmic_probability_network_prior", "graph_entropy_distribution_dependence", "compressed_program_nonrandomness_witness", "maximum_entropy_refinement", ], "rrc_use": "separate apparent graph entropy from generator-law compressibility", "claim_boundary": "algorithmic-information prior; needs computable local compressor witness", "status": "CANDIDATE", }, { "candidate_id": "MDPI.ENTROPY.MULTIDIMENSIONAL_NETWORK_DISTORTION.2021.0001", "title": "Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks", "journal": "Entropy", "year": 2021, "url": "https://www.mdpi.com/1099-4300/23/7/835", "doi": "10.3390/e23070835", "density_markers": [ "multidimensional_network_complexity", "lossless_compression_graph_distortion", "node_alignment_effect", "multilayer_network_information_content", ], "rrc_use": "warn when aligning language/code graph layers changes algorithmic information", "claim_boundary": "distortion prior; local alignment receipts required", "status": "HOLD", }, { "candidate_id": "MDPI.ENTROPY.UNIQUE_INFORMATION.2014.0001", "title": "Quantifying Unique Information", "journal": "Entropy", "year": 2014, "url": "https://www.mdpi.com/70176", "doi": "10.3390/e16042161", "density_markers": [ "shared_unique_synergistic_information", "partial_information_decomposition_prior", "marginal_invariance_property", "redundancy_synergy_split", ], "rrc_use": "separate shared density markers from language-specific unique markers", "claim_boundary": "information-decomposition prior; requires local variable definitions", "status": "CANDIDATE", }, { "candidate_id": "MDPI.ENTROPY.TOPOLOGICAL_INFORMATION_DATA_ANALYSIS.2019.0001", "title": "Topological Information Data Analysis", "journal": "Entropy", "year": 2019, "url": "https://www.mdpi.com/1099-4300/21/9/869", "doi": "10.3390/e21090869", "density_markers": [ "homological_information_functions", "mutual_information_decomposition_topology", "information_complex", "topological_data_analysis_entropy", ], "rrc_use": "topological lens for density-marker graph decompositions", "claim_boundary": "topological-information prior; needs local graph construction", "status": "HOLD", }, { "candidate_id": "MDPI.ENTROPY.MULTIMODAL_INFORMATION_BOTTLENECK.2026.0001", "title": "A Unified Information Bottleneck Framework for Multimodal Biomedical Machine Learning", "journal": "Entropy", "year": 2026, "url": "https://www.mdpi.com/journal/entropy", "doi": "10.3390/e28040445", "density_markers": [ "information_bottleneck_tradeoff", "modality_redundancy_synergy", "fusion_collapse_diagnostic", "transfer_entropy_sequence_prior", ], "rrc_use": "analogy for multi-language/code modality fusion and redundancy scoring", "claim_boundary": "journal listing/abstract prior; verify article page before promotion", "status": "HOLD", }, ] def packetize(candidate: dict[str, Any]) -> dict[str, Any]: packet = { "schema": "mdpi_density_marker_candidate_v1", "source_family": "MDPI", "rrc_shape_hint": "LanguageSetMILPEProjection", **candidate, } packet["packet_hash"] = sha256_text(stable_json(packet)) return packet def csv_escape(value: Any) -> str: text = str(value).replace('"', '""') return f'"{text}"' def main() -> None: OUT_DIR.mkdir(parents=True, exist_ok=True) packets = [packetize(candidate) for candidate in CANDIDATES] JSONL.write_text("\n".join(stable_json(packet) for packet in packets) + "\n", encoding="utf-8") lines = ["candidate_id,title,journal,year,status,density_markers,rrc_use,url,doi,packet_hash"] for packet in packets: lines.append( ",".join( [ csv_escape(packet["candidate_id"]), csv_escape(packet["title"]), csv_escape(packet["journal"]), csv_escape(packet["year"]), csv_escape(packet["status"]), csv_escape(";".join(packet["density_markers"])), csv_escape(packet["rrc_use"]), csv_escape(packet["url"]), csv_escape(packet["doi"]), csv_escape(packet["packet_hash"]), ] ) ) CSV.write_text("\n".join(lines) + "\n", encoding="utf-8") status_counts: dict[str, int] = {} for packet in packets: status_counts[packet["status"]] = status_counts.get(packet["status"], 0) + 1 receipt = { "schema": "mdpi_density_marker_miner_receipt_v1", "claim_boundary": "Metadata-only MDPI mining surface; candidates are priors until local replay, residual, and byte-law receipts exist.", "candidate_count": len(packets), "status_counts": status_counts, "jsonl": str(JSONL.relative_to(REPO)), "csv": str(CSV.relative_to(REPO)), "candidate_ids": [packet["candidate_id"] for packet in packets], } receipt["receipt_hash"] = sha256_text(stable_json(receipt)) RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") print(json.dumps(receipt, indent=2, sort_keys=True)) if __name__ == "__main__": main()