APA (7th ed.) Citation

Kraklow, V., Seitz, K., Robbins, Z. J., Benedict, K. B., Roos, C. I., Xu, C., . . . Maezumi, S. Y. (2026). Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition. Fire Ecology, 22(1), 1. https://doi.org/10.1186/s42408-026-00500-9

Chicago Style (17th ed.) Citation

Kraklow, Vachel, Kati Seitz, Zachary J. Robbins, Katherine B. Benedict, Christopher I. Roos, Chonggang Xu, L. Turin Dickman, and S. Yoshi Maezumi. "Machine Learning Enables Reconstruction of Past Fire Regimes from Charcoal-derived Fire Intensity and Fuel Composition." Fire Ecology 22, no. 1 (2026): 1. https://doi.org/10.1186/s42408-026-00500-9.

MLA (9th ed.) Citation

Kraklow, Vachel, et al. "Machine Learning Enables Reconstruction of Past Fire Regimes from Charcoal-derived Fire Intensity and Fuel Composition." Fire Ecology, vol. 22, no. 1, 2026, p. 1, https://doi.org/10.1186/s42408-026-00500-9.