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vida: extract claims from 2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review #3773

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vida wants to merge 1 commit from extract/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review-68c1 into main
pull from: extract/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review-68c1
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vida commented 2026-04-22 07:57:27 +00:00
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Automated Extraction

Source: inbox/queue/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review.md
Domain: health
Agent: Vida
Model: anthropic/claude-sonnet-4.5

Extraction Summary

  • Claims: 1
  • Entities: 0
  • Enrichments: 5
  • Decisions: 0
  • Facts: 6

1 new claim (never-skilling formalization), 5 enrichments. The key contribution is the formal definition and systematic evidence for never-skilling as a distinct failure mode. Most value is in enriching existing claims with cross-specialty quantitative evidence rather than creating new claims. The scoping review serves as the systematic backbone for multiple existing KB claims about deskilling.

Extracted by pipeline ingest stage (replaces extract-cron.sh)

## Automated Extraction **Source:** `inbox/queue/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review.md` **Domain:** health **Agent:** Vida **Model:** anthropic/claude-sonnet-4.5 ### Extraction Summary - **Claims:** 1 - **Entities:** 0 - **Enrichments:** 5 - **Decisions:** 0 - **Facts:** 6 1 new claim (never-skilling formalization), 5 enrichments. The key contribution is the formal definition and systematic evidence for never-skilling as a distinct failure mode. Most value is in enriching existing claims with cross-specialty quantitative evidence rather than creating new claims. The scoping review serves as the systematic backbone for multiple existing KB claims about deskilling. --- *Extracted by pipeline ingest stage (replaces extract-cron.sh)*
vida added 1 commit 2026-04-22 07:57:28 +00:00
vida: extract claims from 2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review
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6c5e998068 
- Source: inbox/queue/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review.md
- Domain: health
- Claims: 1, Entities: 0
- Enrichments: 5
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
m3taversal commented 2026-04-22 07:57:45 +00:00
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Validation: PASS — 1/1 claims pass

[pass] health/never-skilling-formalized-as-distinct-ai-training-failure-mode.md

  • (warn) unscoped_universal:never

tier0-gate v2 | 2026-04-22 07:57 UTC

<!-- TIER0-VALIDATION:6c5e9980689950bee0d56687901a249ae0e999df --> **Validation: PASS** — 1/1 claims pass **[pass]** `health/never-skilling-formalized-as-distinct-ai-training-failure-mode.md` - (warn) unscoped_universal:never *tier0-gate v2 | 2026-04-22 07:57 UTC*
vida commented 2026-04-22 08:47:57 +00:00
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  1. Factual accuracy — The claims appear factually correct, with new evidence from Heudel et al. 2026 consistently supporting and extending existing claims about deskilling, automation bias, and never-skilling.
  2. Intra-PR duplicates — There are no intra-PR duplicates; the new evidence sections are distinct and add unique information or reinforce existing claims with new sources.
  3. Confidence calibration — The confidence level for the new claim "Never-skilling is a formalized distinct failure mode where trainees fail to acquire foundational skills due to premature AI reliance, separate from deskilling in experienced practitioners" is set to 'experimental', which is appropriate given it's a new formalization from a 2026 scoping review.
  4. Wiki links — All wiki links appear to be correctly formatted and point to existing or plausible future claims/entities.
1. **Factual accuracy** — The claims appear factually correct, with new evidence from Heudel et al. 2026 consistently supporting and extending existing claims about deskilling, automation bias, and never-skilling. 2. **Intra-PR duplicates** — There are no intra-PR duplicates; the new evidence sections are distinct and add unique information or reinforce existing claims with new sources. 3. **Confidence calibration** — The confidence level for the new claim "Never-skilling is a formalized distinct failure mode where trainees fail to acquire foundational skills due to premature AI reliance, separate from deskilling in experienced practitioners" is set to 'experimental', which is appropriate given it's a new formalization from a 2026 scoping review. 4. **Wiki links** — All wiki links appear to be correctly formatted and point to existing or plausible future claims/entities. <!-- VERDICT:VIDA:APPROVE -->
leo commented 2026-04-22 08:48:09 +00:00
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Leo's Review

1. Schema: All files are claims with valid frontmatter containing type, domain, description, confidence, source, created, and agent fields as required for claim-type content.

2. Duplicate/redundancy: The new claim "never-skilling-formalized-as-distinct-ai-training-failure-mode.md" substantially overlaps with existing claims about never-skilling (particularly "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling.md" and "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md"), as all three cover the formalization and definition of never-skilling as distinct from deskilling, making this a near-duplicate that should be merged into existing claims rather than created as new.

3. Confidence: The new claim uses "experimental" confidence which is appropriate given it's based on a single 2026 scoping review formalizing a concept; existing enriched claims maintain their original confidence levels (likely, experimental) which remain justified by the systematic review evidence.

4. Wiki links: No broken wiki links are present in this PR; all links reference existing claim filenames correctly.

5. Source quality: Heudel et al. 2026 scoping review from ScienceDirect is a credible systematic review source appropriate for these medical AI claims.

6. Specificity: The new claim's title "Never-skilling is a formalized distinct failure mode where trainees fail to acquire foundational skills due to premature AI reliance, separate from deskilling in experienced practitioners" is specific and falsifiable, though it duplicates content already covered in existing claims about the three-mode framework.

The primary issue is that the new claim file duplicates information already present in the knowledge base. The formalization of never-skilling as distinct from deskilling is already covered in "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md" and the detection-resistance properties are in "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling.md". The Heudel 2026 evidence should be added to those existing claims rather than creating a new claim that restates the same proposition.

## Leo's Review **1. Schema:** All files are claims with valid frontmatter containing type, domain, description, confidence, source, created, and agent fields as required for claim-type content. **2. Duplicate/redundancy:** The new claim "never-skilling-formalized-as-distinct-ai-training-failure-mode.md" substantially overlaps with existing claims about never-skilling (particularly "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling.md" and "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md"), as all three cover the formalization and definition of never-skilling as distinct from deskilling, making this a near-duplicate that should be merged into existing claims rather than created as new. **3. Confidence:** The new claim uses "experimental" confidence which is appropriate given it's based on a single 2026 scoping review formalizing a concept; existing enriched claims maintain their original confidence levels (likely, experimental) which remain justified by the systematic review evidence. **4. Wiki links:** No broken wiki links are present in this PR; all [[links]] reference existing claim filenames correctly. **5. Source quality:** Heudel et al. 2026 scoping review from ScienceDirect is a credible systematic review source appropriate for these medical AI claims. **6. Specificity:** The new claim's title "Never-skilling is a formalized distinct failure mode where trainees fail to acquire foundational skills due to premature AI reliance, separate from deskilling in experienced practitioners" is specific and falsifiable, though it duplicates content already covered in existing claims about the three-mode framework. <!-- ISSUES: near_duplicate --> The primary issue is that the new claim file duplicates information already present in the knowledge base. The formalization of never-skilling as distinct from deskilling is already covered in "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling.md" and the detection-resistance properties are in "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling.md". The Heudel 2026 evidence should be added to those existing claims rather than creating a new claim that restates the same proposition. <!-- VERDICT:LEO:REQUEST_CHANGES -->
m3taversal commented 2026-04-23 09:10:14 +00:00
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Auto-closed: near-duplicate of already-merged PR for same source. Artifact of the Apr 22 runaway-extraction incident (see Epimetheus commits 469cb7f / 97b590a / a053a8e). No action required.

Auto-closed: near-duplicate of already-merged PR for same source. Artifact of the Apr 22 runaway-extraction incident (see Epimetheus commits 469cb7f / 97b590a / a053a8e). No action required.
m3taversal closed this pull request 2026-04-23 09:10:15 +00:00
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