Google Drive Download Count

the drives contain the raid info. So if you had 4 drives and try to repair with 3, it is missing one.

If you go to two drives that also changes form raid 5 to raid 1.

That is why you have to clean the drives to start over with the ISO.

But clean erases everything. So if you are trying to get to data DO NOT clean

The drives are impossible to find so I do not know where you got them and wonder if they might be counterfeit??? Use something like CrystalDiskInfo to see if the report the proper info. This is not a compatible drive, just a for example

Google Drive Download Count

Download Zip 🔥 https://shoxet.com/2y68jV 🔥

So did you do diskpart clean with the panam.iso?

You are doing the middle step recreate correct?

Put the log file from the thumb drive in a dropbox share and post the link here for me to look at it

In spite of the popularity of drive counts, so far there have been few attempts to evaluate their applicability for estimating animal density/population size. McCullough (1979) used drive counts in his study of a white-tailed deer (Odocoileus virginianus Zimmermann) population. He compared results of drive counts with population size estimated from the age or death of individuals, and concluded that at low population density drive counts underestimated the population relative to age reconstruction, while at high densities it tended to overestimate. However, McCullough (1979) tested drive counts on an enclosed population (driven individuals remained within the area), which probably limits his conclusions for free-ranging populations. Pucek et al. (1975) compared drive counts with snow tracking, and concluded that the latter provides lower density estimates than drive counts, although they did not test the efficiency of drive counts as such. Cederlund et al. (1998) in general found that drive counts and other methods derived from hunting practices were unreliable, and pointed out that double counting, especially at high densities, is hard to avoid. Staines and Ratcliffe (1987) found that deer could be hard to flush from cover, and suggested that drive counting be limited to small areas owing to difficulties in co-ordinating large numbers of beaters and counters. On the basis of existing knowledge, it is therefore hard to draw any clear conclusions regarding the effectiveness of drive counts. In Poland, the method is recommended for use by game managers (Nasiadka 1994), and is probably the most commonly used method for estimating deer populations and trends.

Usually, at least 10% of the total forest area is recommended to be covered by drive counts (Pucek et al. 1975; Nasiadka 1994). However, little is in fact known of how the total area and number of blocks driven influence the results. Similarly, there is no information on how population density and group size affect the results of drive counts. Answering these questions through field studies, however, would be challenging. Even if it were logistically possible to conduct field experiments to examine the effects of such variables, their influence on accuracy cannot be determined unless the true population size is known, which is rarely the case (e.g. Daniels 2006). However, counting methods may be compared in a computer simulation in which total population size is controlled (Smart et al. 2004).

Prior to conducting sample counts on the virtual populations, the effects of random variation and of the grouping probability z were examined in a series of trials on spatially unstructured populations to determine whether the simulation algorithm could generate realistically distributed deer populations.

Examples of randomly generated simulated deer populations and counting blocks in an 18,000 ha virtual forest: spatially unstructured, moderately aggregated populations of a red deer at 10/100 ha and b roe deer at 7.5/100 ha; spatially auto-correlated and demographically aggregated low-density populations of c red deer at 5/100 ha and d roe deer at 2.5/100 ha. Individual squares represent 20 ha forest compartments. The same randomly generated pattern of 30 counting blocks each comprising three adjacent compartments is superimposed on each population. The high frequency of zero counts for the highly aggregated, low-density populations c and d is clearly illustrated

The accuracy of simulated estimated counts of a red deer and b roe deer in relation to density. The accuracy index shows the proportion of counts falling within a specified percentage of the true population total. Each count covered 10% of the forest using 30 blocks of 60 ha each. Means were derived from 20 replicate virtual forests

The accuracy of estimated counts of a red deer at 10/100 ha and b roe deer at 7.5/100 ha in relation to demographic aggregation (low parameter values cause greater aggregation). The accuracy index shows the proportion of counts falling within a specified percentage of the true population total

For each experiment and each deer species, the accuracy index is presented for the highest and lowest value of the range of the effect varied in the experiment, together with the improvement in accuracy (i.e. difference) across the effect range. The accuracy index represents the mean percentage of counts (across 20 simulated populations) for which the estimated population fell within a specified range (30%, 20%, or 10%) of the true population total

The accuracy of estimated counts of a red deer at 10/100 ha and b roe deer at 7.5/100 ha in relation to the degree of spatial auto-correlation (low parameter values cause greater auto-correlation). The accuracy index shows the proportion of counts falling within a specified percentage of the true population total

The accuracy of estimated counts of low-density populations of a red deer (4/100 ha) and b roe deer (3/100 ha) and high-density populations of c red deer (12/100 ha) and d roe deer (20/100 ha) in relation to the size of counting blocks. Populations were demographically aggregated and spatially auto-correlated. The accuracy index shows the proportion of counts falling within a specified percentage of the true population total

The accuracy of estimated counts of low-density spatially aggregated populations of a red deer (4/100 ha) and b roe deer (3/100 ha) and high-density spatially aggregated populations of c red deer (12/100 ha) and d roe deer (20/100 ha) in relation to the proportion of the forest counted. The accuracy index shows the proportion of counts falling within a specified percentage of the true population total. Means were averaged over 60 and 100 ha block sizes

As already mentioned, our simulations took no account of measurement error, owing to lack of empirical data. In real counts, deer theoretically could be either over- or underestimated. However, when 10% of the area is counted and the blocks are driven towards blocks previously counted, it seems that the risk of double counting is minimal. If blocks are distributed regularly throughout the forest, they will be far enough from each other that fleeing individuals are unlikely to stop in as-yet undriven blocks. Alternatively, there are two sources of error that could lead to underestimation. Firstly, some animals may leave a block if disturbed by observers and beaters taking up position around the edge of the block. If this were so, recorded deer density in larger blocks, in which animals should less likely be disturbed (as there is less edge per unit area), should on average be higher than in smaller ones, all other factors being equal. In such a situation, driving of larger blocks might be recommended. Flight behaviour can vary considerably between, and even within, individuals (Sunde et al. 2009), but flight distances of roe deer have been found generally to be less than 100 m on average (de Boer et al. 2004), substantially less than the dimension of typical counting blocks. Moreover, as we found no effect of block area on estimated density within the block for either species in our analysis of data from Polish forests, nor any effect on the probability of recording at least one animal within the block, underestimation due to observer disturbance seems unlikely. Secondly, animals might remain undetected within a driven block. Unfortunately, we have no data on this issue. This may occur especially in areas where animals are accustomed to human presence and therefore reluctant to flee. On the other hand, even in areas where animals tolerate people well and flight distance is short, flight frequency increases when people behave in an unusual way (for instance walk away from trails) (Borkowski 2001). Moreover, owing to their relative sizes, we consider that this issue may be much less important for red deer that for roe deer. No matter how animals behave in reality, maintaining close proximity between beaters to maintain visual contact between them, even in blocks with relatively poor visibility, and using a dog to flush deer from dense cover, should reduce the chance of this sort of error (but see Staines and Ratcliffe 1987). As the size of blocks has little effect on drive count accuracy, block size should be adjusted depending on visibility and number of participants. Poor-visibility blocks should be smaller and driven by a relatively large number of beaters, while surrounded by fewer observers. To compensate, the number of blocks should be increased to maintain the total proportion of forest counted. In addition, drive counts should be organized during the leafless winter period, when visibility in most areas is better.

Our field data showed that levels of aggregation of red and roe deer differ markedly. Red deer distributions were more clumped than those of roe deer, even though daytime counts probably represented mostly inactive individuals. As the more gregarious species, the degree of aggregation of red deer is probably higher than that of roe deer even in the case of inactive individuals. To some extent, differences in the distributions of both species may arise from dissimilarities in habitat use (spatial auto-correlation) evoked by availability of food (Palmer and Truscott 2003) and/or cover (Borkowski 2004; Borkowski and Ukalska 2008). It has been demonstrated, for instance, that red deer as the larger species may be more demanding toward cover condition than smaller roe deer (Borkowski and Ukalska 2008). This may be especially important for resting individuals during day time, i.e. for animals predominately recorded using drive counts. 17dc91bb1f