Hasil untuk "Ecology"

R. P. McIntosh, P. Greig-Smith

B. Chabot, D. J. Hicks

M. Palmer, R. Ambrose, N. Poff

R. Bryant, S. Bailey

M. Katti, J. Marzluff, R. Bowman et al.

R. Stevenson, M. Bothwell, R. Lowe

W. R. Mccabe, G. Jackson

D. Richardson

S. Schnitzer, F. Bongers

C. Combes, I. Buron, V. Connors

J. Lichstein, T. Simons, S. Shriner et al.

A. Sugden

S. Cooke, S. Hinch, M. Wikelski et al.

P. Alpert, E. Bone, Claus Holzapfel

S. Piertney, M. Oliver

A. Larkum, R. Orth, C. Duarte

M. Ungar

J. Koricheva, J. Gurevitch

Mingzhuang Wang, Yvyang Li, Xiyang Zhang et al.

Coral reefs, crucial for sustaining marine biodiversity and ecological processes (e.g., nutrient cycling, habitat provision), face escalating threats, underscoring the need for efficient monitoring. Coral reef ecological monitoring faces dual challenges of low efficiency in manual analysis and insufficient segmentation accuracy in complex underwater scenarios. This study develops the YH-MINER system, establishing an intelligent framework centered on the Multimodal Large Model (MLLM) for "object detection-semantic segmentation-prior input". The system uses the object detection module (mAP@0.5=0.78) to generate spatial prior boxes for coral instances, driving the segment module to complete pixel-level segmentation in low-light and densely occluded scenarios. The segmentation masks and finetuned classification instructions are fed into the Qwen2-VL-based multimodal model as prior inputs, achieving a genus-level classification accuracy of 88% and simultaneously extracting core ecological metrics. Meanwhile, the system retains the scalability of the multimodal model through standardized interfaces, laying a foundation for future integration into multimodal agent-based underwater robots and supporting the full-process automation of "image acquisition-prior generation-real-time analysis".

Eshta Bhardwaj, Rohan Alexander, Christoph Becker

The accelerating development and deployment of AI technologies depend on the continued ability to scale their infrastructure. This has implied increasing amounts of monetary investment and natural resources. Frontier AI applications have thus resulted in rising financial, environmental, and social costs. While the factors that AI scaling depends on reach its limits, the push for its accelerated advancement and entrenchment continues. In this paper, we provide a holistic review of AI scaling using four lenses (technical, economic, ecological, and social) and review the relationships between these lenses to explore the dynamics of AI growth. We do so by drawing on system dynamics concepts including archetypes such as "limits to growth" to model the dynamic complexity of AI scaling and synthesize several perspectives. Our work maps out the entangled relationships between the technical, economic, ecological and social perspectives and the apparent limits to growth. The analysis explains how industry's responses to external limits enables continued (but temporary) scaling and how this benefits Big Tech while externalizing social and environmental damages. To avoid an "overshoot and collapse" trajectory, we advocate for realigning priorities and norms around scaling to prioritize sustainable and mindful advancements.