Paper year
2025
Detect emerging, bridge-candidate, and undercited papers inside a curated audio-ML corpus, then expose the signals behind every recommendation.
Paper dossier
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Paper year
2025
Citations
1
Authors
4
Topic labels
3
Source readout
Journal of the Audio Engineering Society
jaes
Core corpus
Not available yet
Ranking readout
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
3
Top 50
3
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
In top 50 at rank 15
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.7597, citation_velocity=0.0600, topic_growth=1.0000, diversity_penalty=0.0000
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1519)
Recent attention: low; used in final ranking (contribution to score: 0.0300)
Topic momentum: high; used in final ranking (contribution to score: 0.3000)
Cross-cluster signal: not computed for this run
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
In top 50 at rank 1
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0600, topic_growth=1.0000, diversity_penalty=0.0000
Semantic match: not computed for this run
Recent attention: low; used in final ranking (contribution to score: 0.0210)
Topic momentum: high; used in final ranking (contribution to score: 0.6500)
Cross-cluster signal: not computed for this run
Topic breadth penalty: reduces score when non-zero (contribution to score: 0.0000)
Under-cited
In top 50 at rank 4
Low-cite candidate pool (see docs/candidate-pool-low-cite.md v0): core corpus, recency floor, citation ceiling, title+abstract gate; popularity penalty among pool members only. Semantic and bridge not yet modeled.
Signals: citation_velocity=0.0600, topic_growth=1.0000, diversity_penalty=0.2789
Semantic match: not computed for this run
Recent attention: low; used in final ranking (contribution to score: 0.0180)
Topic momentum: high; used in final ranking (contribution to score: 0.7000)
Cross-cluster signal: not computed for this run
Pool popularity penalty: reduces score when non-zero (contribution to score: -0.0697)
Reproducing the main features of a spring reverb tank impulse response across the hearing range with a physical model presents unique challenges because of the high levels of coupling between the spring's vibrational polarizations. Previous attempts based on a model that includes helix angle can accurately simulate helical spring vibrations but see discrepancies in reproducing measured impulse responses due to heavy simplifications in specifying boundary conditions and input/output mechanisms. This paper presents an improved physical modeling approach by incorporating magnetic bead dynamics and frequency-dependent damping. The beads are modeled as coupled beams using a thin form of the spring equations that reduces to thin beam equations in the absence of curvature. Also ensuring the correct geometric alignment between the beads and the spring, the model's response to rotationally driving the input bead is shown to display the expected mixture of waves traveling along the different spring polarizations. To achieve a similar damping profile as observed in measured impulse responses, different damping parameters are set for each polarization, leading to nonproportional damping and multiple decay rates within small frequency bands. The new formulation results in the main features of measured impulse responses now being reproduced well.
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Spacecraft and Cryogenic TechnologiesFluid Dynamics Simulations and InteractionsOil and Gas Production Techniques
Neighbor surface
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
No embedding-backed neighbors available for this paper/version yet.
Next handoff
01
Use Recommended to see whether this paper behaves like an emerging or undercited signal in the current ranked feed, or how it appears on the bridge preview / diagnostics view.
02
Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.
03
Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.