Growth, Mortality and Exploitation Rate of Pagellus bellottii Harvested from the Marine Territorial Waters of Sierra Leone

The study investigated growth, mortality and exploitation rate of Pagellus bellottii collected off Sierra Leone, aimed to support efficient management of its stock. A total of 8, 216 specimens of Pagellus bellottii were collected at random from January-November, 2016 on-board a demersal trawler. Data analysis employed methods implemented in the computerized FiSAT II software using pooled length-frequencies in constant class size. Growth parameters gave asymptotic length (L∞ = 33.63cm), growth rate (K = 0.63yr-1), growth performance index (ɸ = 2.85), theoretical age (to = -0.6years) and life-span (tmax = 5.50years) whereas the length (Lm50) and age (tm50) at first maturity were estimated as 22.40 cm and 2.30years respectively. Besides, the current fishing mortality rate (F = 6.58yr-1) exceeded the optimum (Fopt = 0.50yr-1), limiting (Flimit = 0.81yr-1) and natural (M = 1.22yr-1) mortality rates, and the current exploitation rate (Ecurrent) was 0.84yr-1. Results depicted low life expectancy, fast growth and late first maturity (Lm50 = 33.4%, but < L∞) in Pagellus bellottii. Also, the stock of P. bellottii was overexploited (Ecurrent > 0.5), buttressed by the alarming current fishing mortality beyond optimal and permissible limits. This was very worrisome and the current exploitation rate should be reduced to 0.5yr-1 (40.5%) and fishing mortality rate to 0.5yr-1 (92.4%) in order to achieve at least optimal level of exploitation (Ecurrent = 0.5) and fishing mortality rate (Fcurrent = Fopt) respectively, through institution of closed fishing seasons and reduction in fleets targeting the species.Keywords: Exploitation; growth; life-span; mortality; stock

Dealignment: Class in Britain and Class in British Sociology Since 1945

This paper sets changes in Britain’s class structure since 1945 alongside the parallel sociological controversies about class. Since the 1970s, the class scheme developed by John Goldthorpe and colleagues for initial use in their study of social mobility in Britain has become sociology’s standard template for thinking about and researching class. Versions have been adopted by the UK government and the European Union as their official socio-economic classifications. This paper does not dispute that the Goldthorpe scheme is still the best available for classifying by occupation, or that occupation remains our best single indicator of class, or that a constant class scheme must be used if the purpose is to measure trends over time in rates of relative inter-generational mobility. Despite these merits, it is argued that the sociological gaze has been weakened by failing to represent changes over time in the class structure itself and, therefore, how class is experienced in lay people’s lives. There has been a relative neglect of absolute social mobility flows (which have changed over time), and a pre-occupation with the inter-generational and a relative neglect of intra-career mobilities and immobilities.

Unassisted Noise-Reduction of Chemical Reactions Data Sets

<div><div><div><p>Existing deep learning models applied to reaction prediction in organic chemistry are able to reach extremely high levels of accuracy (> 90% for NLP- based ones1). With no chemical knowledge embedded than the information learnt from reaction data, the quality of the data sets plays a crucial role in the performance of the prediction models. While human curation is prohibitively expensive, the need for unaided approaches to remove chemically incorrect entries from existing data sets is essential to improve the performance of artificial intelligence models in synthetic chemistry tasks. Here we propose a machine learning-based, unassisted approach to remove chemically wrong entries (noise) from chemical reaction collections. Results show that models trained on cleaned and balanced data sets improve the quality of the predictions without a decrease in performance. For the retrosynthetic models the round-trip accuracy is enhanced by 13% and the value of the cumulative Jensen Shannon metric is lowered down to 70% of its original value, while maintaining high values of coverage (97%) and constant class-diversity (1.6) at inference.</p></div></div></div>

Unassisted Noise-Reduction of Chemical Reactions Data Sets

<div><div><div><p>Existing deep learning models applied to reaction prediction in organic chemistry are able to reach extremely high levels of accuracy (> 90% for NLP- based ones1). With no chemical knowledge embedded than the information learnt from reaction data, the quality of the data sets plays a crucial role in the performance of the prediction models. While human curation is prohibitively expensive, the need for unaided approaches to remove chemically incorrect entries from existing data sets is essential to improve the performance of artificial intelligence models in synthetic chemistry tasks. Here we propose a machine learning-based, unassisted approach to remove chemically wrong entries (noise) from chemical reaction collections. Results show that models trained on cleaned and balanced data sets improve the quality of the predictions without a decrease in performance. For the retrosynthetic models the round-trip accuracy is enhanced by 13% and the value of the cumulative Jensen Shannon metric is lowered down to 70% of its original value, while maintaining high values of coverage (97%) and constant class-diversity (1.6) at inference.</p></div></div></div>