Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instructions. However, existing IR benchmarks focus on a limited scope of tasks, making them insufficient for evaluating the latest IR models. In this paper, we propose MAIR (Massive Instructed Retrieval Benchmark), a heterogeneous IR benchmark that includes 126 distinct IR tasks across 6 domains, collected from existing datasets. We benchmark state-of-the-art instruction-tuned text embedding models and re-ranking models. Our experiments reveal that instruction-tuned models generally achieve superior performance compared to non-instruction-tuned models on MAIR. Additionally, our results suggest that current instruction-tuned text embedding models and re-ranking models still lack effectiveness in specific long-tail tasks. MAIR is publicly available at https://github.com/sunnweiwei/Mair.
Abstract. The main processes underlying the generation and maintenance of biodiversity include both local factors such as competition and abiotic filtering and regional forces such as paleoclimate, speciation and dispersal. While the effects of regional and local drivers on species diversity are increasingly studied, their relative importance for other aspects of diversity, notably phylogenetic and functional diversity is so far little studied. Here, we link data from large Chinese forest plots to data on current and Last Glacial Maximum (LGM) climate as well as local disturbance regimes to study their relative roles in determining woody plant phylogenetic and functional diversity in this important hotspot for woody plant diversity. Local disturbance was the best predictor of functional diversity as represented by maximum canopy height (Hmax), probably reflecting the dominant role of competition for light in determining the forest Hmax structure. In contrast, the LGM–present anomaly in temperature was the factor with the strongest explanatory power for phylogenetic diversity, with modern climate also important. Hence, local contemporary and regional historical factors have highly contrasting importance for the geographic patterns of the functional (as represented by variation in maximum canopy height) and phylogenetic aspects of Chinese forest's woody plant diversity. Importantly, contemporary factors are of overriding importance for functional diversity, while paleoclimate has left a strong signature in the phylogenetic diversity patterns.
Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.
The act of upside-down in Mo Yan'《Upside Down》is an image with a sense of metaphor and symbolization, providing for the readers a unique visual angle to see the art world and understand its implication. In the construction of the plot,《Upside Down》has its own reasonable development, which greatly supports the pattern of the works.
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