Standard Practice for Examining Magnetic Card Readers

10.1520/e3017 ◽  
2015 ◽  
Author(s):  
1969 ◽  
Vol 62 (1_Suppl) ◽  
pp. S113-S133 ◽  
Author(s):  
Sam Brody

ABSTRACT This report is a summary of 10 years of experience with the complement fixation test as adopted for the immunoassay of HCG in serum. It is based on published as well as unpublished material. The discussion centers mainly around methodological problems, criteria of reliability, and clinical observations. It is our impression that the complement fixation test is a reasonably rapid and simple technical procedure. It is standard practice in every bacteriological and virological laboratory. The precision of the HCG assay is high. Its accuracy is good. The complement fixation assay, as reported here, fulfils the criteria of specificity. It has been evaluated by means of serological techniques and through comparison between biopotency and immunopotency of HCG in serum with reference to a common standard. Its application for routine as well as research work is illustrated.


1970 ◽  
Vol 20 ◽  
Author(s):  
R. Goossens

Contribution to the automation of the calculations involving  the forest inventory with the aid of an office computer - In this contribution an attempt was made to perform the  calculations involving the forest inventory by means of an office computer  Olivetti P203.     The general program (flowchart 1), identical for all tree species except  for the values of the different parameters, occupies the tracks A and B of a  magnetic card used with this computer. For each tree species one magnetic  card is required, while some supplementary cards are used for the  subroutines. The first subroutine (flowchart 1) enables us to preserve  temporarily the subtotals between two tree species (mixed stands) and so  called special or stand cards (SC). After the last tree species the totals  per ha are calculated and printed on the former, the average trees occuring  on the line below. Appendix 1 gives an example of a similar form resulting  from calculations involving a sampling in a mixed stand consisting of Oak  (code 11), Red oak (code 12), Japanese larch (code 24) and Beech (code 13).  On this form we find from the left to the right: the diameter class (m), the  number of trees per ha, the basal area (m2/ha), the current annual increment  of the basal area (m2/year/ha), current annual volume increment (m3/year/ha),  the volume (m3/ha) and the money value of the standing trees (Bfr/ha). On the  line before the last, the totals of the quantities mentioned above and of all  the tree species together are to be found. The last line gives a survey of  the average values dg, g, ig, ig, v and w.     Besides this form each stand or plot has a so-called 'stand card SC' on  wich the totals cited above as well as the area of the stand or the plot and  its code are stored. Similar 'stand card' may replace in many cases  completely the classical index cards; moreover they have the advantage that  the data can be entered directly into the computer so that further  calculations, classifications or tabling can be carried out by means of an  appropriate program or subroutine. The subroutine 2 (flowchart 2) illustrates  the use of similar cards for a series of stands or eventually a complete  forest, the real values of the different quantities above are calculated and  tabled (taking into account the area). At the same time the general totals  and the general mean values per ha, as well as the average trees are  calculated and printed. Appendix 2 represents a form resulting from such  calculations by means of subroutine 2.


2021 ◽  
pp. 008117502098263
Author(s):  
Sasha Shen Johfre ◽  
Jeremy Freese

Social scientists often present modeling results from categorical explanatory variables, such as gender, race, and marital status, as coefficients representing contrasts to a “reference” group. Although choosing the reference category may seem arbitrary, the authors argue that it is an intrinsically meaningful act that affects the interpretability of results. Reference category selection foregrounds some contrasts over others. Also, selecting a culturally dominant group as the reference can subtly reify the notion that dominant groups are the most “normal.” The authors find that three of four recently published tables in Demography and American Sociological Review that include race or gender explanatory variables use dominant groups (i.e., male or white) as the reference group. Furthermore, the tables rarely state what the reference is: only half of tables with race variables and one-fifth of tables with gender variables explicitly specify the reference category; the rest leave it up to the reader to check the methods section or simply guess. As an alternative to this apparently standard practice, the authors suggest guidelines for intentionally and responsibly choosing a reference category. The authors then discuss alternative ways to convey results from categorical explanatory variables that avoid the problems of reference categories entirely.


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