diff --git a/image_analysis/scale_merge/ScaleAndMerge.cpp b/image_analysis/scale_merge/ScaleAndMerge.cpp
index 6b60439b..d2027c8c 100644
--- a/image_analysis/scale_merge/ScaleAndMerge.cpp
+++ b/image_analysis/scale_merge/ScaleAndMerge.cpp
@@ -390,11 +390,9 @@ namespace {
     }
 
     void merge(size_t nhkl, ScaleMergeResult &out, const std::vector<ObsRef> &obs) {
-        // ---- Classical crystallographic merging: inverse-variance weighted mean ----
-        // For each observation:  I_corr = I_obs / correction
-        //                        sigma_corr = sigma_obs / correction
-        //                        w = 1 / sigma_corr^2 = correction^2 / sigma_obs^2
-
+       // Merging
+        // For weighting, we are extra multiplying weight by total correction value to down-weight reflections
+        // which come from very weak images and/or low partiality
         struct HKLAccum {
             double sum_wI = 0.0;   // sum of w * I_corr
             double sum_w  = 0.0;   // sum of w
@@ -406,7 +404,7 @@ namespace {
                 continue;
             const double I_corr    = static_cast<double>(o.r->I) * o.correction;
             const double sigma_corr = o.sigma * o.correction;
-            const double w         = 1.0 / (sigma_corr * sigma_corr);
+            const double w         = o.correction / (sigma_corr * sigma_corr);
             auto &a = accum[o.hkl_slot];
             a.sum_wI += w * I_corr;
             a.sum_w  += w;
@@ -769,3 +767,96 @@ ScaleMergeResult ScaleAndMergeReflectionsCeres(const std::vector<std::vector<Ref
 
     return out;
 }
+
+ScaleMergeResult MergeReflections(const std::vector<std::vector<Reflection>> &observations,
+                                   const ScaleMergeOptions &opt) {
+    size_t nrefl = 0;
+    for (const auto &i: observations)
+        nrefl += i.size();
+
+    std::vector<ObsRef> obs;
+    obs.reserve(nrefl);
+
+    std::unordered_map<HKLKey, int, HKLKeyHash> hklToSlot;
+    hklToSlot.reserve(nrefl);
+
+    for (int i = 0; i < (int)observations.size(); i++) {
+        for (const auto &r: observations[i]) {
+            if (!std::isfinite(r.I) || !std::isfinite(r.d) || r.d <= 0.0f)
+                continue;
+            if (opt.d_min_limit_A > 0.0 && r.d < opt.d_min_limit_A)
+                continue;
+            if (!std::isfinite(r.rlp) || r.rlp == 0.0f)
+                continue;
+            if (r.partiality <= opt.min_partiality_for_merge)
+                continue;
+
+            // correction stored as 1 / (G * partiality * LP), with G = 1
+            const double lp         = SafeInv(static_cast<double>(r.rlp), 1.0);
+            const double correction = SafeInv(r.partiality * lp, 0.0);
+            if (correction <= 0.0)
+                continue;
+
+            int hkl_slot;
+            try {
+                const HKLKey key = CanonicalizeHKLKey(r, opt);
+                auto it = hklToSlot.find(key);
+                if (it == hklToSlot.end()) {
+                    hkl_slot = static_cast<int>(hklToSlot.size());
+                    hklToSlot.emplace(key, hkl_slot);
+                } else {
+                    hkl_slot = it->second;
+                }
+            } catch (...) {
+                continue;
+            }
+
+            ObsRef o;
+            o.r          = &r;
+            o.img_id     = i;
+            o.hkl_slot   = hkl_slot;
+            o.sigma      = SafeSigma(r.sigma, opt.min_sigma);
+            o.correction = correction;
+            obs.push_back(o);
+        }
+    }
+
+    const int nhkl = static_cast<int>(hklToSlot.size());
+
+    std::vector<HKLKey> slotToHKL(nhkl);
+    for (const auto &kv: hklToSlot)
+        slotToHKL[kv.second] = kv.first;
+
+    ScaleMergeResult out;
+    out.merged.resize(nhkl);
+    for (int h = 0; h < nhkl; ++h) {
+        out.merged[h].h     = slotToHKL[h].h;
+        out.merged[h].k     = slotToHKL[h].k;
+        out.merged[h].l     = slotToHKL[h].l;
+        out.merged[h].I     = 0.0;
+        out.merged[h].sigma = 0.0;
+        out.merged[h].d     = 0.0;
+    }
+
+    // Populate d from median of observations per HKL
+    {
+        std::vector<std::vector<double>> per_hkl_d(nhkl);
+        for (const auto &o: obs) {
+            const double d_val = static_cast<double>(o.r->d);
+            if (std::isfinite(d_val) && d_val > 0.0)
+                per_hkl_d[o.hkl_slot].push_back(d_val);
+        }
+        for (int h = 0; h < nhkl; ++h) {
+            auto &v = per_hkl_d[h];
+            if (!v.empty()) {
+                std::nth_element(v.begin(), v.begin() + (long)(v.size() / 2), v.end());
+                out.merged[h].d = v[v.size() / 2];
+            }
+        }
+    }
+
+    merge(nhkl, out, obs);
+    stats(opt, nhkl, out, obs);
+
+    return out;
+}
diff --git a/image_analysis/scale_merge/ScaleAndMerge.h b/image_analysis/scale_merge/ScaleAndMerge.h
index d154e941..e92b7e69 100644
--- a/image_analysis/scale_merge/ScaleAndMerge.h
+++ b/image_analysis/scale_merge/ScaleAndMerge.h
@@ -91,4 +91,9 @@ struct ScaleMergeResult {
 };
 
 ScaleMergeResult ScaleAndMergeReflectionsCeres(const std::vector<std::vector<Reflection>>& observations,
-                                              const ScaleMergeOptions& opt = {});
\ No newline at end of file
+                                              const ScaleMergeOptions& opt = {});
+
+/// Merge reflections without any scaling (G = 1, partiality and LP taken as-is from the Reflection).
+/// Uses the same HKL canonicalization and statistics as ScaleAndMergeReflectionsCeres.
+ScaleMergeResult MergeReflections(const std::vector<std::vector<Reflection>>& observations,
+                                  const ScaleMergeOptions& opt = {});
\ No newline at end of file