The Community Interpreter’s Latitude for Action:A Triadic Discourse Interpreting Model(TRIM)

Sociological communication problems associated with the interpreter’s presence and actions in the community have come into the focus of discussion, leading to such opposing views of the interpreter as a “verbatim” reproducer of messages in another language, on the one hand, or as “advocator,” “cultural broker,” or “conciliator,” on the other hand. This essay aims at exploring the interpreter’s latitude for action in a given situation. In order to discuss this essay, static and dynamic parameters are established and described in a Triadic Discourse Interpreting Model (TRIM) when an interpreter reproduces a target message. This interplay is assumed to take place in the form of a number of interpreting filters (IF) through which a source message (M) passes to become a target message (M’). Within the model, the filters interrelate static components and dynamic components as decision-making stages to become different types of M’ (zero M’, partially invariant M’ with two categories, variant M’, invariant M’). The model derived checklist thus helps the interpreter to anticipate potential problems and pre-establish strategies to secure an adequate action for a planned assignment.

Algal community structure prediction by machine learning

The algal community structure is vital for aquatic management.However,the complicated environmental and biological processes make modeling challenging.To cope with this difficulty,we investigated using random forests(RF)to predict phytoplankton community shifting based on multi-source environmental factors(including physicochemical,hydrological,and meteorological variables).The RF models robustly predicted the algal communities composed by 13 major classes(Bray-Curtis dissimilarity=9.2±7.0%,validation NRMSE mostly<10%),with accurate simulations to the total biomass(validation R^(2)>0.74)in Norway's largest lake,Lake Mjosa.The importance analysis showed that the hydro-meteorological variables(Standardized MSE and Node Purity mostly>0.5)were the most influential factors in regulating the phytoplankton.Furthermore,an in-depth ecological interpretation uncovered the interactive stress-response effect on the algal community learned by the RF models.The interpretation results disclosed that the environmental drivers(i.e.,temperature,lake inflow,and nutrients)can jointly pose strong influence on the algal community shifts.This study highlighted the power of machine learning in predicting complex algal community structures and provided insights into the model interpretability.

Multivariate Analysis, Description, and Ecological Interpretation of Weed Vegetation in the Summer Crop Fields of Anhui Province, China

Two surveys were conducted to investigate weed vegetation in a 153-hm^2 sampling area of summer crop fields from Anhui Province, China, through visual scoring of the level of weed infestation compared with summer crops on a seven-class scale. In total, 155 sampling sites were selected in the field based on crops, tillage, rotation systems, geographical regions, and soil types across the province. Data on weed communities and environmental factors were collected and analyzed through principal component analysis （PCA） and canonical correspondence analysis （CCA）, and the output was interpreted ecologically. Results showed that the main factors influencing the structure and distribution of weed communities in summer crop fields were the soil submersion period, latitude, and soil type and pH. The CCA indicated a significant relationship between weed dominance and soil submersion duration, latitude, and soil pH. From the result of the PCA and CCA ordination, the 155 sampling sites could be divided into three groups based on geographic and floristic composition, as well as weed abundance. The southern dry land group, which was characterized by a double-cropping system in the hilly regions of southern and central Anhui Province with a continuous summer crop and an autumn dry land crop, was dominated by Galium aparine Linn. var. tenerum （Gren. et Godr） Robb., Avenafatua L., and Veronica persica Poir. The northern dry land group, which had the same cropping system as the southern dry land group, was dominated by G. aparine var. tenerum, Galium tricorne Stokes, Descurainia sophia （L.） Schur., and Lithospermum arvense L. in the North Anhui Province, China. These two dry land groups could be combined into one large dry land group, in which the Galium weed vegetation type dominated. The third group was the paddy soil group, which was characterized by a continu- ous summer crop and double- or triple-cropping systems of rice, and prevailed in the south and central areas of Anhui Province; Alopecurus aequalis Sobol. was the dominant weed in this group. Other main weeds in this group included Malachium aquaticum （L.） Fries, Stellaria alsine Grimm, Alopecurusjaponicus Steud., and Lapsana apogonoides Maxim. Thus, the weed community distributions in this group were described as the Alopecurus weed vegetation type. The paddy soil group could be divided into two subgroups, one southern and one central paddy soil subgroup. A strategy for integrated weed management is suggested according to the weed distribution pattern. The present study serves as a good example of how a quantitative research method was used to associate a visual estimate of weed infestation with multivariate analyses, such as PCA and CCA, and how this method can be applied to the study of weed vegetation on arable land.