Log fleet_k_q48/fleet_conserved; K holds exactly, 775/775 ticks (FABRIC-3.md §XVIII)

doe_log.c's per-heartbeat-tick CSV gains two columns: fleet_k_q48
(vm_physics_fleet_heat_sum() over ALL live VMs -- the genuine fleet-wide
conservation invariant K, not reconstructable from the 3 named-Tripod-
member heat columns already logged, which omit every identity VM's own
heat) and fleet_conserved (vm_physics_conserved() as 0/1). Requested
explicitly after the first heartbeat-telemetry analysis pass
(analysis-20260912/) omitted K entirely.

Kernel rebuilt on all three architectures, full 3x9x3 campaign rerun
(results-20260912-with-k/). K = 1.0000000000 (Q48.16 raw 65536) on every
one of 775 heartbeat-tick observations, sd(K) = 0, 100% fleet_conserved,
across amd64/aarch64/riscv64, nine identities, three replicates -- zero
deviation. Also a free regression check on both recent Stadium fixes
(§XVI/§XVII): neither disturbed the reservoir-transfer accounting K
depends on.

Found and fixed a tooling wrinkle along the way: fleet_conserved, being
the CSV row's very last field with nothing after it to bound a regex
match, can have a resumed trial digit merge into it with zero separator
on the wire -- combine.py now derives it from fleet_k_q48 directly (same
epsilon vm_physics_conserved() uses) instead of trusting the raw field.
fleet_k_q48 itself is unaffected either way.

Full analysis, discussion, and light/dark SVG->PDF figures written up as
a proper LaTeX report (report-20260912/report/std79_doe_report.pdf),
following experiments/bare_metal/analysis/report/bare_metal_doe_report.tex's
established style -- supersedes analysis-20260912/'s markdown-only first
pass as the primary deliverable for this dataset (kept, not discarded).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EXieurDfDSsDFdnSyusuWo

experiments/std79-doe/report-20260912/analysis.R

@@ -0,0 +1,171 @@
+#!/usr/bin/env Rscript
+# analysis.R -- std79-doe 3x9 factorial analysis, K-conservation centerpiece.
+# Run from this directory (contains combined.csv). Produces charts/*.svg
+# (light+dark pairs, matching experiments/bare_metal/analysis's own
+# convention) and tables/*.csv for the LaTeX report in report/.
+
+suppressMessages({
+  library(dplyr)
+  library(tidyr)
+  library(ggplot2)
+  library(svglite)
+})
+
+dir.create("charts", showWarnings = FALSE)
+dir.create("tables", showWarnings = FALSE)
+
+df <- read.csv("combined.csv", stringsAsFactors = FALSE)
+
+id_levels <- c("zuse", "rajames", "00", "01", "02", "03", "04", "05", "06")
+arch_levels <- c("amd64", "aarch64", "riscv64")
+df$id_label <- factor(df$id_label, levels = id_levels)
+df$arch <- factor(df$arch, levels = arch_levels)
+
+df <- df %>%
+  arrange(arch, run_id, tick_number) %>%
+  group_by(arch, run_id) %>%
+  mutate(apic_delta = apic_ticks - lag(apic_ticks)) %>%
+  ungroup()
+
+# ── shared theme, matches experiments/bare_metal/analysis/analyse_bare_metal.R ──
+theme_light_report <- function() {
+  theme_minimal(base_size = 11) %+replace%
+    theme(
+      panel.grid.minor = element_blank(),
+      panel.grid.major = element_line(colour = "grey88"),
+      plot.title = element_text(face = "bold", size = 12),
+      axis.text.x = element_text(angle = 45, hjust = 1)
+    )
+}
+theme_dark_report <- function() {
+  theme_minimal(base_size = 11) %+replace%
+    theme(
+      plot.background = element_rect(fill = "#0d0d0d", colour = NA),
+      panel.background = element_rect(fill = "#0d0d0d", colour = NA),
+      panel.grid.minor = element_blank(),
+      panel.grid.major = element_line(colour = "#1a1a2e"),
+      text = element_text(colour = "#aaaaaa"),
+      axis.text = element_text(colour = "#aaaaaa"),
+      plot.title = element_text(face = "bold", size = 12, colour = "white"),
+      axis.text.x = element_text(angle = 45, hjust = 1, colour = "#aaaaaa"),
+      legend.text = element_text(colour = "#aaaaaa"),
+      legend.background = element_rect(fill = "#0d0d0d")
+    )
+}
+save_svg <- function(p, name, w = 8, h = 6) {
+  ggsave(file.path("charts", paste0(name, ".svg")), p, width = w, height = h, device = svglite)
+}
+
+# ── K / conservation centerpiece: fleet_k across every tick, every arch ──
+cat(sprintf("fleet_k range: [%.10f, %.10f], distinct values: %d\n",
+            min(df$fleet_k), max(df$fleet_k), length(unique(df$fleet_k))))
+cat(sprintf("fleet_conserved: %d/%d rows TRUE\n", sum(df$fleet_conserved == 1), nrow(df)))
+
+make_k_plot <- function(dark = FALSE) {
+  bg <- if (dark) "#0d0d0d" else "white"
+  line_col <- if (dark) "#00e5ff" else "#1f77b4"
+  ref_col <- if (dark) "#888888" else "grey50"
+  p <- ggplot(df, aes(x = tick_number, y = fleet_k, colour = arch)) +
+    geom_hline(yintercept = 1.0, linetype = "dashed", colour = ref_col, linewidth = 0.4) +
+    geom_point(size = 0.5, alpha = 0.6) +
+    facet_wrap(~arch, ncol = 1, scales = "free_x") +
+    scale_y_continuous(limits = c(0.9, 1.1)) +
+    labs(title = "Fleet conservation invariant K over the whole campaign",
+         subtitle = "K = sum(execution_heat_q48) over all live VMs; dashed line = Q48_ONE (perfect conservation)",
+         x = "Heartbeat tick number", y = "K") +
+    theme(legend.position = "none")
+  if (dark) p <- p + theme_dark_report() + theme(legend.position = "none")
+  else      p <- p + theme_light_report() + theme(legend.position = "none")
+  p
+}
+save_svg(make_k_plot(FALSE), "fleet_k_light", w = 9, h = 8)
+save_svg(make_k_plot(TRUE),  "fleet_k_dark",  w = 9, h = 8)
+
+# ── per-cell (arch x id_label) summary + ANOVA for every other metric ──
+metrics <- c(
+  "hot_word_count", "avg_word_heat", "window_width", "actual_window_size",
+  "jitter_ns", "apic_delta", "time_trust", "variance", "vm_call_depth_max",
+  "hera_heat", "hermes_heat", "artemis_heat"
+)
+metric_labels <- c(
+  hot_word_count = "Hot word count",
+  avg_word_heat = "Mean word execution heat",
+  window_width = "Rolling window width",
+  actual_window_size = "Actual analysis window size",
+  jitter_ns = "Estimated timer jitter (ns)",
+  apic_delta = "APIC ticks per heartbeat tick",
+  time_trust = "TIME-TRUST",
+  variance = "Timing variance",
+  vm_call_depth_max = "Max VM call depth",
+  hera_heat = "Hera fleet heat",
+  hermes_heat = "Hermes fleet heat",
+  artemis_heat = "Artemis fleet heat"
+)
+
+cell_summary <- function(metric) {
+  df %>%
+    filter(!is.na(.data[[metric]])) %>%
+    group_by(arch, id_label) %>%
+    summarise(n = n(), mean = mean(.data[[metric]]), sd = sd(.data[[metric]]),
+              cv_pct = ifelse(mean != 0, 100 * sd / abs(mean), NA_real_), .groups = "drop")
+}
+anova_table <- function(metric) {
+  d <- df %>% filter(!is.na(.data[[metric]]))
+  if (length(unique(d[[metric]])) <= 1) return(NULL)
+  fit <- tryCatch(aov(as.formula(paste0(metric, " ~ arch * id_label")), data = d),
+                   error = function(e) NULL)
+  if (is.null(fit)) return(NULL)
+  summary(fit)[[1]]
+}
+make_plot <- function(metric) {
+  d <- df %>% filter(!is.na(.data[[metric]]))
+  if (length(unique(d[[metric]])) <= 1) return(NULL)
+  p <- ggplot(d, aes(x = id_label, y = .data[[metric]], fill = id_label)) +
+    geom_boxplot(outlier.size = 0.6, alpha = 0.85) +
+    facet_wrap(~arch, ncol = 1) +
+    labs(title = metric_labels[[metric]], x = "Identity", y = metric_labels[[metric]]) +
+    theme(legend.position = "none")
+  p_light <- p + theme_light_report() + theme(legend.position = "none")
+  p_dark  <- p + theme_dark_report() + theme(legend.position = "none")
+  save_svg(p_light, paste0(metric, "_light"), w = 7, h = 8)
+  save_svg(p_dark,  paste0(metric, "_dark"),  w = 7, h = 8)
+  TRUE
+}
+
+anova_rows <- list()
+cell_rows <- list()
+for (metric in metrics) {
+  cs <- cell_summary(metric)
+  cs$metric <- metric
+  cell_rows[[metric]] <- cs
+
+  at <- anova_table(metric)
+  if (!is.null(at)) {
+    at_df <- as.data.frame(at)
+    at_df$term <- trimws(rownames(at))
+    at_df$metric <- metric
+    anova_rows[[metric]] <- at_df
+  }
+  make_plot(metric)
+}
+
+write.csv(bind_rows(cell_rows), "tables/cell_summary.csv", row.names = FALSE)
+write.csv(bind_rows(anova_rows), "tables/anova.csv", row.names = FALSE)
+
+# ── overall dataset summary table (per architecture) ──
+overall <- df %>%
+  group_by(arch) %>%
+  summarise(
+    n_rows = n(),
+    n_trials = n_distinct(run_id),
+    elapsed_s = max(elapsed_ns) / 1e9,
+    mean_hot_words = mean(hot_word_count),
+    mean_fleet_k = mean(fleet_k),
+    sd_fleet_k = sd(fleet_k),
+    pct_conserved = 100 * mean(fleet_conserved == 1),
+    .groups = "drop"
+  )
+write.csv(overall, "tables/overall_summary.csv", row.names = FALSE)
+
+cat("\nDone. charts/, tables/ written.\n")
+print(as.data.frame(overall))