|By Marketwired .||
|July 3, 2014 11:30 AM EDT||
LONDON, UNITED KINGDOM and BAIE VERTE, NL -- (Marketwired) -- 07/03/14 -- Rambler Metals and Mining plc (TSX VENTURE: RAB) (LSE: RMM) ('Rambler' or the 'Company') today announces that it has delivered approximately 5,300 wet metric tonnes ('wmt') of copper and gold concentrate from Goodyear's Cove, NL Canada.
The Company's internal sampling estimates an average grade for the shipment of 27.8 per cent copper, 9.9 grammes per tonne gold and 82 grammes per tonne silver. This is the third shipment during the calendar year, the seventh completed to date bringing the total concentrate delivered to approximately 46,800 wmt.
Following the shipment, the Company still has over 1,400 wmt of concentrate in storage at its Goodyear's Cove Facility.
Norman Williams, President and CEO, commented:
"Since the initial start of production in 2012 we have produced and shipped over 46,000 tonnes of high grade copper concentrate. We have been working to improve gold recovery at the mill since our last shipment in April which can account for the slightly higher amounts of precious metals in this batch.
"With just one month left to our July 31 year-end we are on track to exceed the forecasted guidance for concentrate production."
ABOUT RAMBLER METALS AND MINING
Rambler is a mining and development Company that in 2012 brought its first mine into commercial production. The group has a 100 per cent ownership in the Ming Copper-Gold Mine, a fully operational base and precious metals processing facility and year round bulk storage and shipping facility; all located on the Baie Verte peninsula, Newfoundland and Labrador, Canada.
The Company's Vision is to be Atlantic Canada's leading mine operator and resource developer through the expansion of the Ming Mine, discovering new deposits and through mergers and acquisitions. Rambler listed on the London AIM in 2005 and Toronto TSX-V in 2007.
Click on, or paste the following link into your web browser, to view the associated PDF document.
Sep. 29, 2016 10:30 PM EDT Reads: 4,021
Sep. 29, 2016 10:15 PM EDT Reads: 2,786
Sep. 29, 2016 10:00 PM EDT Reads: 1,804
Sep. 29, 2016 09:45 PM EDT Reads: 3,126
Sep. 29, 2016 08:45 PM EDT Reads: 1,546
Sep. 29, 2016 08:45 PM EDT Reads: 2,209
Sep. 29, 2016 06:15 PM EDT Reads: 3,685
Sep. 29, 2016 06:00 PM EDT Reads: 1,537
Sep. 29, 2016 05:15 PM EDT Reads: 2,855
Sep. 29, 2016 05:15 PM EDT Reads: 1,583
Sep. 29, 2016 04:45 PM EDT Reads: 2,794
Sep. 29, 2016 04:45 PM EDT Reads: 3,446
Sep. 29, 2016 04:30 PM EDT Reads: 1,343
Sep. 29, 2016 04:30 PM EDT Reads: 1,962
So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, provided tips on how to be successful in large scale machine learning...
Sep. 29, 2016 04:00 PM EDT Reads: 2,402