GSDAW Experiment Section Writer — generates Phase 4 (Experiment) paragraph files. Triggered by /aw-write-experiment or by aw-execute during wave execution. Reads .planning/methodology.md (datasets, baselines, metrics, ablation sections) to generate experiment section paragraphs in Elsevier LaTeX format. Outputs independent paragraph .tex files to sections/experiment/ directory.
name aw-write-experiment description GSDAW Experiment Section Writer — generates Phase 4 (Experiment) paragraph files. Triggered by /aw-write-experiment or by aw-execute during wave execution. Reads .planning/methodology.md (datasets, baselines, metrics, ablation sections) to generate experiment section paragraphs in Elsevier LaTeX format. Outputs independent paragraph .tex files to sections/experiment/ directory. aw-write-experiment — Experiment Section Writer Purpose Write the Experiment section of an academic paper (Phase 4 of IMRAD) by generating independent paragraph files that are later merged into a complete chapter by aw-execute . This skill is a section-writing subagent called by the Wave Executor during Phase 2. It receives a specific task (e.g., "Write 4.1 Dataset Description") and outputs a single paragraph .tex file. When to Trigger aw-execute wave executor calls this skill with a specific task User runs /aw-write-experiment directly Orchestrator delegates during GSDAW pipeline execution Inputs Input Source Description Task description Wave executor (objective field) Which paragraph to write (e.g., "4.1 Dataset Description") research-brief.json .planning/research-brief.json Author intent, novelty claims methodology.md .planning/methodology.md Full experiment design — datasets, baselines, metrics, ablation literature.md .planning/literature.md Related work context (for baseline positioning) Elsevier template templates/elsevier/ LaTeX format reference Outputs Paragraph files written to sections/experiment/ with naming convention {task-id}.tex : File Task ID Content sections/experiment/4-1-datasets.tex 4.1 Dataset description with dataset table sections/experiment/4-2-baselines.tex 4.2 Baseline method configurations sections/experiment/4-3-metrics.tex 4.3 Evaluation metric definitions sections/experiment/4-4-ablation.tex 4.4 Ablation study setup Dataset Table Format \begin{table}[htbp] \centering \caption{Experimental Datasets} \label{tab:datasets} \begin{tabular}{llcccc} \toprule Test Set & Source & Material & Defects & Signal Count & SNR Range \ \midrule Sim-train & FEM simulation & Al + CFRP & FBH, SDH, delamination & 10,000 pairs & -10 to +20 dB \ Sim-val & FEM simulation & Al + CFRP & FBH, SDH, delamination & 1,000 pairs & -10 to +20 dB \ Sim-test & Held-out FEM & Al + CFRP & FBH, SDH, delamination & 500 pairs & -10 to +20 dB \ Al-exp & Experimental & Al 2024-T3 & FBH (3, 5, 8 mm depth) & 50 A-scans & -5 to +5 dB \ CFRP-exp & Experimental & CFRP laminate & Delamination, fiber breakage & 50 A-scans & -8 to +2 dB \ \bottomrule \end{tabular} \end{table} Baseline Configuration Table \begin{table}[htbp] \centering \caption{Baseline Methods} \label{tab:baselines} \begin{tabular}{lll} \toprule Method & Configuration & Implementation \ \midrule Wiener filter & Adaptive, 5x5 local neighborhood & SciPy signal.wiener \ DWT denoising & Daubechies-4, 4-level decomp., soft thresholding & PyWavelets wtmm.denoise \ BM3D & Block matching 3D, 8x8x8 blocks, sigma_est=auto & bm3d package \ Sparse coding & Overcomplete DCT (256 atoms), OMP reconstruction & SPAMS toolbox \ Bandpass filter & Butterworth 4th order, passband 1--10 MHz & SciPy signal.butterworth \ \bottomrule \end{tabular} \end{table} Evaluation Metrics Definitions \subsection{Evaluation Metrics}
We evaluate denoising performance using five metrics.
\textbf{SNR Improvement} measures the gain in signal-to-noise ratio: [ \Delta\text{SNR} = 20 \log_{10}(\text{RMS}{denoised}) - 20 \log{10}(\text{RMS}_{noisy}) ] where RMS denotes the root mean square of the signal amplitude.
\textbf{Mean Squared Error} quantifies the squared difference to the clean reference: [ \text{MSE} = \frac{1}{N}|y_{clean} - y_{denoised}|^2 ]
\textbf{Lin's Concordance Correlation Coefficient} (CCC) measures waveform morphology preservation independent of scale: [ \text{CCC} = \frac{2\text{Cov}(y_{true}, y_{pred})}{\text{Var}(y_{true}) + \text{Var}(y_{pred}) + (\bar{y}{true} - \bar{y}{pred})^2} ]
\textbf{Waveform Similarity Index} (WSI) measures zero-lag normalized cross-correlation between denoised and clean waveforms.
\textbf{F1-score for Defect Detection} evaluates the probability of detecting defect echoes in denoised A-scans via peak detection, at SNR = -5 dB. Workflow aw-write-experiment invoked with task "4.1 Dataset Description" │ ▼ Read: .planning/methodology.md (Experiment Design section) │ ▼ Read: .planning/research-brief.json (novelty) │ ▼ Read: templates/elsevier/ (LaTeX format) │ ▼ Write: sections/experiment/4-1-datasets.tex │ ▼ Return completion with word count and preview Step-by-Step Execution Step 1: Read Inputs Read .planning/methodology.md Section 3 (Experiment Design): 3.1 Datasets (training, validation, test sets) 3.2 Baseline Methods (configurations) 3.3 Evaluation Metrics (definitions) 3.4 Ablation Studies (variants) 3.5 Statistical Analysis (seed, significance testing) Also read .planning/research-brief.json for novelty claims relevant to experimental validation. Step 2: Extract Content per Task 4-1-datasets.tex — Dataset Description: Training set: 10,000 paired noisy-clean A-scan signals from FEM Validation: 1,000 held-out pairs Test sets: Sim-test (500), Al-exp (50), CFRP-exp (50) Materials: Aluminum 2024-T3 (60%), CFRP laminate (40%) Defect types: FBH, SDH, delamination, fiber breakage FEM parameters: Aluminum $v_L=6320$ m/s, CFRP $v_L=3000$ m/s Signal specs: 2048 samples at 100 MHz, SNR -10 to +20 dB 4-2-baselines.tex — Baseline Methods: Wiener filter (adaptive, 5x5 neighborhood) DWT denoising (Daubechies-4, 4-level, soft thresholding) BM3D (block matching 3D, 8x8x8 blocks) Sparse coding (overcomplete DCT, 256 atoms, OMP) Bandpass filter (Butterworth 4th order, 1-10 MHz) Include justification for why these are fair comparisons 4-3-metrics.tex — Evaluation Metrics: SNR improvement: $\Delta\text{SNR}$ target 8-12 dB MSE: target < 1.5 x 10^-3 CCC: target > 0.95 WSI: target > 0.90 F1-score: target > 0.85 at -5 dB POD: Probability of Detection, slope > 2.5 dB^-1 4-4-ablation.tex — Ablation Studies: A1: Loss function (MSE-only vs. MSE+CCC vs. Full) A2: Architecture depth (3-level vs. 4-level vs. 5-level) A3: Skip connections (no skip vs. concat vs. concat+attention) A4: Training SNR distribution (fixed +10 dB vs. uniform -10 to +20 dB) A5: Input window size (256 vs. 512 vs. 1024) A6: Attention gating (with vs. without) Step 3: Write Paragraph File Write the .tex file with: \paragraph{Section Title} heading with label Running text with technical detail Tables using booktabs format Metric definitions with inline math Cross-references via \ref{tab:} Step 4: Verify Output At least 150 words per paragraph Academic register Elsevier citation format Tables use booktabs No hardcoded numbers in \ref{} No TODO/FIXME placeholders Step 5: Report Completion Paragraph 4.1 (Dataset Description) written. Word count: 312 File: sections/experiment/4-1-datasets.tex Preview: "The training dataset consists of 10,000 paired noisy-clean A-scan signals..." Elsevier LaTeX Conventions Same as aw-write-methodology : \documentclass[review]{elsarticle} \usepackage{booktabs} for tables \cite{key} for numbered citations \ref{tab:} , \ref{fig:} , \ref{eq:} for cross-references Error Handling Missing methodology.md 错误：未找到方法论设计文件 (.planning/methodology.md)。
请先运行 /aw-methodology 生成实验设计， 或确认方法论已通过 Discuss #2 审批。 Incomplete Experiment Design If a section is missing content: Write available content with gap note Report in completion message File Locations manuscripts/[paper-name]/ ├── .planning/ │ ├── research-brief.json │ ├── methodology.md ← Primary input │ └── literature.md ├── templates/elsevier/ └── sections/ └── experiment/ ├── 4-1-datasets.tex ← Output ├── 4-2-baselines.tex ← Output ├── 4-3-metrics.tex ← Output └── 4-4-ablation.tex ← Output Integration Points Connection Agent/File Direction Called by aw-execute (Wave Executor) Input: task Feeds into aw-execute (Phase Merger) Output: paragraphs Reads .planning/methodology.md Input Later review aw-review After wave Quality Gate Checklist At least 150 words in paragraph Academic register Elsevier citation format Dataset table uses booktabs Baseline table uses booktabs Metric definitions include formulas No hardcoded numbers in cross-references No TODO/FIXME placeholders File saved to correct path sections/experiment/{filename}.tex