## Research Purpose And Boundary This part preserves the individual scalar attribution and correlation inventory for the frozen Task 43 raw-inclusive hybrid. The root reference owns the benchmark claim, component-head performance, family totals, mapping contrast, population stability, and external comparison. This part owns only the 37 scalar rows and their measured overlap on the fixed 5,042-row training matrix. Mean absolute TreeSHAP measures use by the existing nonlinear ensemble. The Pearson values measure association. Neither quantity is an additive QWK contribution, causal effect, or feature-removal result. ## Evidence And Sources - Canonical component result: `.orchestration/context/sessions/01a02917-0817-773e-90d4-43870e2002b7/evidence/task-hule-20-44-03/component-audit-result-v3.json`, SHA-256 `79e9f8c1d17ec6fa36e3d035cf620d0468462b8190628bc4cd6383733f185bbb`. - Ordered scalar inventory: `.orchestration/context/sessions/01a02917-0817-773e-90d4-43870e2002b7/evidence/task-hule-20-44-03/scalar-features-v3.jsonl`, SHA-256 `111b11ea70c85ccaa8f68744aad17ffcfed5ce8205b0f35be82f1aeb35c3166e`. - Population: the fixed 3,479 base plus 1,563 candidate training rows used by the frozen raw-inclusive hybrid. - Attribution: member-averaged TreeSHAP from the five frozen 805-column hybrid models. Its ensemble reconstruction maximum absolute error is `0.00000477`. - Correlation: Pearson correlation on the same 5,042 rows. `Max |r|` is the maximum absolute correlation between one scalar and any one of the 768 embedding coordinates. ## Findings And Interpretation Four scalar features share the accepted DeBERTa runtime lineage: `sent_similarity_variance`, `prompt_similarity`, `intro_prompt_sim`, and `min_para_relevance`. The remaining 33 scalars do not consume that embedding runtime. | Construct family | Scalar feature | Mean absolute TreeSHAP | Max \|r\| | | ------------------------------------- | ---------------------------------------------------- | ---------------------: | --------: | | Correction/error | `grammar_errors_per_100_words` | 0.001296 | 0.500 | | Correction/error | `language_tool_spelling_errors_per_100_words` | 0.017286 | 0.660 | | Correction/error | `final_spelling_issues_per_100_words` | 0.002690 | 0.678 | | Correction/error | `punctuation_errors_per_100_words` | 0.000000 | 0.281 | | Readability | `smog` | 0.000183 | 0.329 | | Readability | `coleman_liau` | 0.001813 | 0.584 | | Readability | `ari` | 0.003098 | 0.383 | | Readability | `avg_sentence_length` | 0.001825 | 0.428 | | Lexical statistics | `ttr` | 0.000103 | 0.660 | | Length | `word_count` | 0.052873 | 0.378 | | Length | `avg_word_length` | 0.001016 | 0.591 | | Syntax | `parse_tree_depth` | 0.000403 | 0.472 | | Syntax | `passive_ratio` | 0.000367 | 0.218 | | Syntax | `dep_distance` | 0.000984 | 0.424 | | Semantic coherence, embedding-derived | `sent_similarity_variance` | 0.000377 | 0.161 | | Prompt relevance, embedding-derived | `prompt_similarity` | 0.000761 | 0.305 | | Prompt relevance, embedding-derived | `intro_prompt_sim` | 0.000165 | 0.240 | | Prompt relevance, embedding-derived | `min_para_relevance` | 0.000000 | 0.286 | | Complex nominal | `procedural_or_event_head_complex_np_density_log1p` | 0.000095 | 0.264 | | RST relation | `tier3_rst_gum_primary_tree_satellite_relation_rate` | 0.001632 | 0.332 | | Clause rates | `finite_advcl_per_100w` | 0.000885 | 0.259 | | Clause rates | `finite_relative_clause_per_100w` | 0.000128 | 0.234 | | Clause rates | `nonfinite_participial_advcl_per_100w` | 0.000321 | 0.205 | | Clause rates | `nonfinite_participial_acl_per_100w` | 0.000000 | 0.172 | | Frequency/sophistication | `p_lex_lambda` | 0.000175 | 0.423 | | Frequency/sophistication | `advanced_guiraud` | 0.018843 | 0.487 | | Word-range dispersion | `mean_log_contextual_diversity` | 0.000213 | 0.466 | | Word-range dispersion | `mean_contextual_diversity_percent` | 0.000000 | 0.496 | | Word-range dispersion | `contextual_diversity_coverage` | 0.000000 | 0.352 | | Moving-average lexical diversity | `mattr_50` | 0.000000 | 0.566 | | Concreteness | `mean_concreteness` | 0.000390 | 0.461 | | Concreteness | `concreteness_coverage` | 0.000511 | 0.304 | | Word prevalence | `mean_word_prevalence` | 0.000231 | 0.257 | | Word prevalence | `word_prevalence_coverage` | 0.000507 | 0.283 | | Lexical decision | `mean_lexical_decision_rt_ms` | 0.000728 | 0.424 | | Lexical decision | `lexical_decision_rt_coverage` | 0.002162 | 0.252 | | Causal-predicate complex nominal | `abstract_np_with_causal_predicate` | 0.000376 | 0.244 | No scalar is constant, and no two scalar columns are exact duplicates. Eight scalar pairs have absolute Pearson correlation at least `0.8`: | Scalar pair | r | | ------------------------------------------------------------------------------------- | -----: | | `ari` / `avg_sentence_length` | 0.988 | | `mean_log_contextual_diversity` / `mean_contextual_diversity_percent` | 0.953 | | `coleman_liau` / `avg_word_length` | 0.945 | | `concreteness_coverage` / `word_prevalence_coverage` | 0.936 | | `language_tool_spelling_errors_per_100_words` / `final_spelling_issues_per_100_words` | 0.935 | | `ari` / `parse_tree_depth` | 0.833 | | `avg_sentence_length` / `parse_tree_depth` | 0.832 | | `p_lex_lambda` / `mean_log_contextual_diversity` | -0.813 | The high correlations include shared denominators and overlapping constructs, but correlation alone does not establish that one feature is removable. The TreeSHAP values likewise describe the fitted ensemble on this matrix; they do not report performance after removing a feature. ## Evidence Gaps And Follow-Up No matched per-feature removal fits were run. They were outside Task 03 and would add 37 post-hoc model-selection comparisons. The three declared reduced heads and the root reference's family attribution are the bounded component evidence for this story.