Military / Defense | SEAMS Newsletter | BLOCK VECTOR Technologies

Receipts

Run stamp	20260313_103926
Tool version	v348
SEAM model	seamv1
Generated	2026-03-13 10:39:35
Total rows scanned	11172
Unique records	8369
Duplicate rate	25.1%
Seam candidate rate (deduped)	0.1%
Median views	8
Median downloads	2
Median conversion	0.106
Median citations	1

What these numbers mean

Click for definitions

Total rows scanned: total sampled rows across slices (rows × sorts × pages × terms). Not deduped.
Unique records: deduped count of unique record_id values in the consolidated dataset.
Duplicate rate: 1 − (unique / total). High duplication usually means the same papers repeat across sorts/buckets/pages.
SEAM candidate rate: candidates / unique (deduped) based on the SEAM model threshold.
Median views/downloads/conversion: robust central tendency across the current run’s unique records.
Top seam tells: the most common seam signals seen in the candidate set.
New keyword candidates: terms disproportionately associated with seam candidates (heuristic, min sample size).
Exclude/downweight terms: high-volume terms with low median conversion (heuristic, min sample size).

Engagement Comparison

	Rows	Unique	Median Views	Median Downloads	Median Conversion
Candidates	22	14	16	13	0.193
Non-candidates	11150	8355	8	2	0.106

Run Funnel

A quick read on where rows are being filtered out between collection, text availability, tell detection, near-miss status, and final candidate status.

Bucket Yield

Bronze shows total rows per bucket. Steel overlay shows candidate + near-miss yield so weak buckets stand out quickly.

SEAM Candidate Articles

Article	Bucket/Sources	Score	Views	DL	Cites	Evidence/snippet
Decentralised Runtime Monitoring for Access Control Systems in Cloud Federations	`H_trust_boundary_zta:mostviewed`	78	161	139	26	therefore, in order to promote accountability and transparency of access control decisions in federated clouds, we present a decentralised runtime monitoring...
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring	`E_interface_icd:bestmatch\|E_interface_icd:mostdownloaded\|E_interface_icd:mostviewed`	46	197	179	12	in this paper, we propose an approach to use dnn uncertainty estimators to implement such supervisor. we first discuss advantages and disadvantages of e...
Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring	`E_interface_icd:bestmatch\|E_interface_icd:mostviewed`	46	109	42	12	lastly, we discuss a large-scale study conducted on four different subjects to empirically validate the approach, reporting the lessons-learned as guidance f...
Humans as Safety Constraints: A Survey of Human-in-the-Loop Reinforcement Learning for Critical Systems	`D_custom:bestmatch\|D_custom:mostrecent\|D_custom:mostviewed`	42	65	45	10	unlike traditional human-in-the-loop rl approaches that focus on learning efficiency, this work emphasizes human oversight to prevent catastrophic outcomes i...
Remote Supervisory Control for a Robotic Container Handling System	`D_custom:bestmatch`	39	32	38	13	during the mmi platform meeting on the 8th of march 2022, tno colleagues represented moses project, by delivering a presentation entitled:  remote super...
Design and simulation of a TTRH-EV supervisory controller for a proton SAGA 1.3	`D_custom:bestmatch`	39	33	26	13	a supervisory controller design and implementation for retrofitted ttrh-ev saga is based on the bsfc map and ool of the engine.
The Use of the Simplex Architecture to Enhance Safety in Deep-Learning-Powered Autonomous Systems	`E_interface_icd:newest`	22	0	0	22
Overview and Performance Evaluation of Supervisory Controller Synthesis with Eclipse ESCET v4.0	`D_custom:newest`	13	0	0	13
Explainable AI-Enhanced Supervisory Control for Robust Multi-Agent Robotic Systems	`D_custom:newest`	13	0	0	13
Towards Policy Enforcement Point as a Service (PEPS)	`B_systems_resilience:newest`	11	0	0	11
Research on risk prevention and control of coal mine gas explosion using bayesian network and system dynamics: An optimization model for safety investment decision-making	`A_autonomous_ai_safety:newest\|E_interface_icd:newest\|H_trust_boundary_zta:newest`	10	0	0	10
kevinkawchak/national-mcp-pai-oncology-trials: v0.9.0 - Phase 4: Integration Adapters, Clinical Safety Guardrails, and Robot Execution Boundaries	`B_systems_resilience:newest\|H_trust_boundary_zta:newest`	10	0	0	10
Lost in Localization: Building RabakBench with Human-in-the-Loop Validation to Measure Multilingual Safety Gaps	`D_custom:newest`	10	0	0	10
Dual-Modality IoT Framework for Integrated Access Control and Environmental Safety Monitoring with Real-Time Cloud Analytics	`H_trust_boundary_zta:newest`	10	0	0	10

Top Seam Tells

Tell	Count
1	22
domain	17
gate+action+domain	14
safety	11
fail-safe	5
safe execution	5
safe state	5
17	5
human-in-the-loop	4
supervisory control	4

Note: Some sources do not provide abstracts/metrics; “tells” may be title-derived for those rows.

Near-Miss Diagnostics

Article	Bucket	Source	Tells	Views	DL	Why not candidate
Green-engineered rare-earth–doped ZnO-Y2O3 nanocomposites for efficient textile dye degradation: Operational parameter, environmental toxicity evaluation, and agricultural safety assessment	`F_dsm_integration_structure`	crossref	3	0	0	No conversion; No metrics
Crashworthiness of a composite fuselage barrel and related certification aspects: Evaluation of the passengers safety under critical drop test conditions by numerical simulations	`F_dsm_integration_structure`	crossref	3	0	0	No conversion; No metrics
A Reference Architecture for Integrating Safety and Security Applications on Railway Command and Control Systems	`A_autonomous_ai_safety`	zenodo	2	1407	1029	Near threshold
A Reference Architecture for Integrating Safety and Security Applications on Railway Command and Control Systems	`A_autonomous_ai_safety`	zenodo	2	1407	1029	Near threshold
A Reference Architecture for Integrating Safety and Security Applications on Railway Command and Control Systems	`A_autonomous_ai_safety`	zenodo	2	1407	1029	Near threshold
A Reference Architecture for Integrating Safety and Security Applications on Railway Command and Control Systems	`A_autonomous_ai_safety`	zenodo	2	1407	1029	Near threshold
Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"	`F_dsm_integration_structure`	zenodo	2	1569	186	Near threshold
Abhijeet Sarkar's The Superintelligence Blueprint: A Critical Analysis of AI Governance, Ethics, Strategic Foresight, Safety, Alignment, Arms Races, and Architectures	`D_custom`	zenodo	2	393	336	Near threshold
Abhijeet Sarkar's The Superintelligence Blueprint: A Critical Analysis of AI Governance, Ethics, Strategic Foresight, Safety, Alignment, Arms Races, and Architectures	`D_custom`	zenodo	2	393	336	Near threshold
Abhijeet Sarkar's The Superintelligence Blueprint: A Critical Analysis of AI Governance, Ethics, Strategic Foresight, Safety, Alignment, Arms Races, and Architectures	`D_custom`	zenodo	2	393	336	Near threshold
Fail-Safe Controller Architectures for Quadcopter with Motor Failures	`E_interface_icd`	zenodo	2	125	472	Near threshold
Fail-Safe Controller Architectures for Quadcopter with Motor Failures	`E_interface_icd`	zenodo	2	125	472	Near threshold
Fail-Safe Controller Architectures for Quadcopter with Motor Failures	`E_interface_icd`	zenodo	2	125	472	Near threshold
RTCA Detect and Avoid Phase 2: Safety Risk Management Modeling and Simulation Final Report	`A_autonomous_ai_safety`	zenodo	2	243	283	Near threshold
RTCA Detect and Avoid Phase 2: Safety Risk Management Modeling and Simulation Final Report	`A_autonomous_ai_safety`	zenodo	2	243	283	Near threshold
RTCA Detect and Avoid Phase 2: Safety Risk Management Modeling and Simulation Final Report	`A_autonomous_ai_safety`	zenodo	2	243	283	Near threshold
RTCA Detect and Avoid Phase 2: Safety Risk Management Modeling and Simulation Final Report	`A_autonomous_ai_safety`	zenodo	2	243	283	Near threshold
Usage Control Policy Enforcement in SDN-based Clouds: A Dynamic Availability Service Use Case	`B_systems_resilience`	zenodo	2	155	357	Near threshold
Usage Control Policy Enforcement in SDN-based Clouds: A Dynamic Availability Service Use Case	`B_systems_resilience`	zenodo	2	155	357	Near threshold
Usage Control Policy Enforcement in SDN-based Clouds: A Dynamic Availability Service Use Case	`B_systems_resilience`	zenodo	2	155	357	Near threshold

Rows shown here are not final SEAM candidates, but they were close enough to help tune thresholds, buckets, and tell logic.

Why rows did not become SEAM candidates

reason	rows	share
Low SEAM score	11150	100.0%
No seam tells detected	9468	84.9%
No measurable conversion	5405	48.5%
No engagement metrics	5186	46.5%
Missing abstract / summary text	1	0.0%
Source likely sparse on abstract/metrics	1	0.0%

These are diagnostic reasons counted across non-candidate rows. A single row can contribute to more than one reason, so the shares are directional rather than additive.

Bucket Performance

bucket	rows	unique	text	tells	near	cand	cand_rate	med_views	med_dl
B_systems_resilience	1687	993	1687	490	488	2	0.1%	10	6
D_custom	1750	1224	1750	303	295	8	0.5%	16	15
A_autonomous_ai_safety	1750	1027	1750	270	269	1	0.1%	18	8
E_interface_icd	1750	1482	1750	242	235	7	0.4%	15	7
H_trust_boundary_zta	1750	1545	1750	155	151	4	0.2%	13	6
C_standards_conformance	735	660	734	155	155	0	0.0%	0	0
F_dsm_integration_structure	1750	1627	1750	89	89	0	0.0%	1	0

Use this to see which buckets are producing rows, text, tells, near-misses, and candidates.

Source Performance

source	rows	unique	text	tells	near	cand	cand_rate	med_views	med_dl
zenodo	6100	3448	6100	730	719	11	0.2%	67	70
arxiv	1572	1556	1572	472	466	6	0.4%	0	0
openalex	1750	1670	1749	278	276	2	0.1%	0	0
crossref	1750	1695	1750	224	221	3	0.2%	0	0

Use this to compare which sources are providing richer data versus sparse metadata.

Top “New Keyword Candidates”

term	n	cand_rate	med_conv
D_custom	1750	0.5%	0.376
E_interface_icd	1750	0.4%	0.248
H_trust_boundary_zta	1750	0.2%	0.234
B_systems_resilience	1687	0.1%	0.286
A_autonomous_ai_safety	1750	0.1%	0.320
F_dsm_integration_structure	1750	0.0%	0.000
C_standards_conformance	735	0.0%	0.000

Heuristic: terms with high seam-candidate association (min 5 samples). Low-sample rows are highlighted and tooltipped.

Top “Exclude / Downweight Terms”

term	n	med_conv
B_systems_resilience	1000	0.714
C_standards_conformance	100	0.750
F_dsm_integration_structure	1000	0.796
H_trust_boundary_zta	1000	0.824
A_autonomous_ai_safety	1000	0.852
E_interface_icd	1000	0.923
D_custom	1000	0.957

Heuristic: Zenodo terms with low median conversion (min 10 samples, cutoff 0.02). Low-sample rows are highlighted and tooltipped.

Receipts

What these numbers mean

Engagement Comparison

Run Funnel

Bucket Yield

SEAM Candidate Articles

Top Seam Tells

Near-Miss Diagnostics

Why rows did not become SEAM candidates

Bucket Performance

Source Performance

Top “New Keyword Candidates”

Top “Exclude / Downweight Terms”

History and trends

Files