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Ultra-Reduced-Impact-Encased-Logging (URIEL): propose a new method for selective sustainable logging and post-harvest silvicultural treatment in tropical forest using airborne robotics systems

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arXiv:2605. 28883v1 Announce Type: new Abstract: Tropical forests worldwide are under intense deforestation pressure driven by economic and political interests, and scientific evidence suggests this deforestation contributes to climate change.

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Authors: Daniel Albiero, Gelton Fernando de Morais, Daniela Han, Fl\'avio Roberto de Freitas Gon\c{c}alves, Artur Vit\'orio Andrade Santos, Wesllen Lins de Ara\'ujo, Alessandra Maia Freire, Cl\'audio Kiyoshi Umezu, Mateus Peressin, Francesco Toscano, Admilson \'Irio Ribeiro, Alfeu J. Sguarezi Filho, Am\'erico Ferraz Dias Neto, Angel Pontin Garcia
Released: 2026-05-29 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Ultra-Reduced-Impact-Encased-Logging (URIEL): propose a new method for selective sustainable logging and post-harvest silvicultural treatment in tropical forest using airborne robotics systems overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, rate, factor. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.28883

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