=== ENVIRONMENT ===
python 3.12.3 | numpy 2.4.4 | pandas 3.0.2 | statsmodels 0.14.6 | scipy 1.17.1 | seed 20260712

=== INPUT HASH VERIFICATION (read-only) ===
Syrian: ROSE_Syrian_bilingual_features_FINAL.xlsx  MATCH  (1b7b3a0660662b64...)
British: ROSE_British_native_features_FINAL.xlsx  MATCH  (7388c56de41c2532...)

Syrian 103 spk / 618 rows; British 30 spk / 90 rows | ref: Arabic, neutral, Sex=F

=== M1 SYRIAN PRIMARY ===
VoicedSeg_s     (1+Language|SpeakerID)conv=True AR=-0.362(SE0.077,p2.4e-06) EN=-0.378(p1.9e-07) INT=-0.016(p0.81)
UnvoicedLen_mean(1+Language|SpeakerID)conv=True AR=+0.397(SE0.074,p8.7e-08) EN=+0.411(p3.4e-08) INT=+0.014(p0.82)
UnvoicedLen_sd  (1|SpeakerID)         conv=True AR=+0.484(SE0.081,p2.7e-09) EN=+0.493(p1.4e-09) INT=+0.009(p0.85)
Loud_mean       (1+Language|SpeakerID)conv=True AR=+0.159(SE0.095,p9.5e-02) EN=+0.104(p3.1e-01) INT=-0.055(p0.22)
Loud_cv         (1+Language|SpeakerID)conv=True AR=-0.100(SE0.079,p2.0e-01) EN=-0.149(p5.9e-02) INT=-0.049(p0.32)
F0_mean         (1+Language|SpeakerID)conv=True AR=-0.017(SE0.036,p6.4e-01) EN=-0.028(p4.7e-01) INT=-0.011(p0.51)
F0_cv           (1+Language|SpeakerID)conv=True AR=-0.038(SE0.065,p5.6e-01) EN=-0.016(p8.5e-01) INT=+0.022(p0.76)
HNR             (1|SpeakerID)         conv=True AR=-0.253(SE0.068,p2.2e-04) EN=-0.294(p1.7e-05) INT=-0.042(p0.55)

=== M2 BRITISH REPLICATION ===
VoicedSeg_s     b=-0.386(SE0.133) CI[-0.648,-0.125] p=0.0038 LOSO[-0.390,-0.365] boot[-0.595,-0.119] boot_fits=67/250
UnvoicedLen_meanb=+0.386(SE0.130) CI[+0.130,+0.641] p=0.0031 LOSO[+0.376,+0.397] boot[+0.082,+0.637] boot_fits=106/250
UnvoicedLen_sd  b=+0.535(SE0.160) CI[+0.222,+0.848] p=0.00082 LOSO[+0.444,+0.625] boot[+0.236,+0.832] boot_fits=242/250
Loud_mean       b=+0.027(SE0.168) CI[-0.303,+0.357] p=0.87 LOSO[-0.023,+0.048] boot[-0.322,+0.336] boot_fits=179/250
Loud_cv         b=-0.041(SE0.174) CI[-0.383,+0.301] p=0.81 LOSO[-0.144,+0.087] boot[-0.422,+0.278] boot_fits=236/250
F0_mean         b=+0.130(SE0.167) CI[-0.198,+0.458] p=0.44 LOSO[+0.058,+0.176] boot[-0.165,+0.392] boot_fits=250/250
F0_cv           b=-0.040(SE0.184) CI[-0.402,+0.321] p=0.83 LOSO[-0.103,+0.035] boot[-0.384,+0.229] boot_fits=243/250
HNR             b=-0.476(SE0.176) CI[-0.820,-0.132] p=0.0067 LOSO[-0.656,-0.364] boot[-0.838,-0.114] boot_fits=250/250

=== M3 STIMULUS SENSITIVITY (omnibus three-way, 2 df) ===
VoicedSeg_s     chi2(2)=5.14 p=0.0766 (1+Language|SpeakerID)
UnvoicedLen_meanchi2(2)=0.14 p=0.933 (1+Language|SpeakerID)
UnvoicedLen_sd  chi2(2)=0.74 p=0.691 (1|SpeakerID)
Loud_mean       chi2(2)=2.65 p=0.266 (1+Language|SpeakerID)
Loud_cv         chi2(2)=1.00 p=0.606 (1+Language|SpeakerID)
F0_mean         chi2(2)=1.26 p=0.533 (1+Language|SpeakerID)
F0_cv           chi2(2)=0.57 p=0.752 (1+Language|SpeakerID)
HNR             chi2(2)=0.55 p=0.76 (1|SpeakerID)

=== CROSS-CONTEXT standardized slopes ===
Syrian_Arabic_L1 VoicedSeg_s:-0.36 UnvoicedLen_mean:+0.42 UnvoicedLen_sd:+0.49 Loud_mean:+0.17 Loud_cv:-0.13 F0_mean:-0.02 F0_cv:-0.05 HNR:-0.25
Syrian_English_L2 VoicedSeg_s:-0.38 UnvoicedLen_mean:+0.39 UnvoicedLen_sd:+0.49 Loud_mean:+0.08 Loud_cv:-0.12 F0_mean:-0.03 F0_cv:+0.01 HNR:-0.30
UK_English_L1 VoicedSeg_s:-0.38 UnvoicedLen_mean:+0.38 UnvoicedLen_sd:+0.53 Loud_mean:+0.03 Loud_cv:-0.04 F0_mean:+0.13 F0_cv:-0.04 HNR:-0.47
saved Forest_plot.pdf / .png

=== WARNINGS SUMMARY (captured) ===
  571  UserWarning
    2  ResourceWarning
    2  ConvergenceWarning