Table A4. Fish Collected Using a Backpack Shocker, Waukegan River, June 5,1996 ID Stations Code Common name Sl S2 Nl N2 NA Alewife 3 3088 Black bullhead 1 2 3120 Bluegill 15 6 NA Coho salmon 2 10 3069 Creek chub 7 1 3065 Fathead minnow 1 2 0000 Xxxxxxx xxxx 0 13 3022 Goldfish 1 3116 Green sunfish 1 3068 Longnose dace 35 5 3156 Mottled sculpin 1 2 NA Ninespine stickleback 3 6 NA Threespine stickleback 48 383 Abundance factor 1 1 5 1 IBI 34.00 28.00 36.00 32.00 61
Table A4. 2: USE Table (2012) This table is extracted from the Use table (2012) of 73 commodities by 71 US industries and Final users, provided by the Bureau of Economic Analysis (BEA). Commodities are shown in rows and industries are presented in columns. Each element shows the value of the commodity in the corresponding row that the industry in the corresponding column uses as the input for its production. The Total Commodity Output is the sum of all entries in a row and the Total Industry Output is the sum of all entries in a column. (Millions of dollars) Commodities/Industries 111CA 113FF 211 … GFE GSLG GSLE F010 … F10N IO Code Name Farms Forestry, fishing, and related activities Oil and gas extraction … Federal govern- ment enterprises State and local general govern- ment State and local govern- ment enterprises Total Intermediate Personal consump- tion expendi- tures … State and local: Gross investment in intellectual property products Total Final Uses (GDP) Total Commodity Output 111CA Farms 68231 560 0 … 3 2317 0 328943 66304 … 0 67033 395976 113FF Forestry, fishing, and related activities 23929 4742 0 … 41 1533 0 57259 5231 … 0 -3867 53391 211 Oil and gas extraction 0 0 21797 … 1189 0 8409 598886 0 … 0 -324178 274708 … … … … … … … … … … … … … … … GSLE State and local government enterprises 0 4 0 … 265 4074 1006 22369 61765 … 0 61765 84135 Used Scrap, used and second- hand goods -44 0 0 … 0 0 0 24444 47979 … 0 -13055 11389 Other Noncomparable imports and rest-of-the-world adjustment 729 55 778 … 1047 0 0 118565 -74655 … 0 -115659 2906 Total Intermediate 249436 12066 73836 … 48250 622222 145208 12507991 0 … 0 0 0 V001 Compensation of employees 27584 20292 34983 … 56694 1180884 94071 8618544 0 … 0 0 0 V002 Taxes on production and imports, less subsidies -1381 1609 31468 … -5124 0 -17402 1074019 0 … 0 0 0 V003 Gross operating surplus 125286 12410 200982 … -1825 178895 42651 6462692 0 … 0 0 0 Total Value Added 151489 34311 267432 … 49745 1359779 119320 0 0 … 0 16155255 0 Total Industry Output 400924 46377 341268 … 97995 1982000 264528 0 11050627 … 30977 0 28663246 Table A4.3: SHARE Table (2012) This table is extracted from the constructed SHARE table (2012), showing the contribution of industries in the supply of commodities. Industries are shown in rows and commodities are displayed in columns. Each element shows the percentage of the total supply of the commodity in the corresponding column accounted for by the industry in the corresponding row. Industries/Com...
Table A4. Patents and Patent applications on relevant bioconversion processes Patents Table A5 List of selected companies PAT. NO Title Application Number Filed Assignee US8,067,552 Method for preparing a target protein using the sHSPs US 11/915,314 May 23 2005 Korea Advanced Institute of Science and Technology US7,629,161 Bioconversion of a fermentable carbon source to 1,3- propanediol by a single microorganism US 11/589,485 Oct 30 2006 E.I. du Pont de Nemours and Company US7,135,309 Processes for the bioconversion of a fermentable carbon source to 1,3-propanediol by a single microorganism US 09/575,638 May 10 1996 E.I. du Pont de Nemours and Company US6,762,302 Process for dehydrogenation of azaandrostane compounds US 10/429,699 May 6 2003 Xxxxxx Xxxxxxx, Ltd. US6,287,828 Method for producing amide compounds using a nitrile hydratase from a thermophilic bacillus US 09/550,394 April 14 2000 Board of Trustees operating Michigan State University US6,242,242 Method for producing amide compounds using a nitrile hydratase from a thermophilic bacillus US 09/549106 April 13 2000 Board of Trustees operating Michigan State University US6,228,633 Method for producing amide compounds using a nitrile hydratase from a thermophilic bacillus US 09/549111 April 13 2000 Board of Trustees operating Michigan State University US6,214,603 Method for producing amide compounds using a nitrile hydratase from a thermophilic bacillus US 09/549,108 April 13 2000 Board of Trustees operating Michigan State University US6,153,415 Method for producing amide compounds using a nitrile hydratase from a thermophilic bacillus US 09/248,528 Feb 10 1999 Board of Trustees operating Michigan State University US6,025,184 Bioconversion of a fermentable carbon source to 1,3- propanediol by a single microorganism US 08/966,794 Nov 10 1997 E.I. du Pont de Nemours and Company, Genencor International Inc. US5,821,092 Production of 1,3-propanediol from glycerol by recombinant bacteria expressing recombinant diol dehydratase US 08/687,852 July 26 1996 E.I. du Pont de Nemours and Company US 5,686,276 Bioconversion of a fermentable carbon source to 1,3- propanediol by a single microorganism US 08/440,293 May 12 1995 E.I. du Pont de Nemours and Company US5,633,362 Production of 1,3-propanediol from glycerol by recombinant bacteria expressing recombinant diol dehydratase US 08/440,377 May 12 1995 E.I. du Pont de Nemours and Company US4,968,612 Continuous fermentation process for aromatic hydrocarbon bioconversion US ...
Table A4. Estimated detection probabilities within the truncation distance of 2000m for common and striped dolphins combined during the ship survey of SCANS III (from Table 18 in Xxxxxxx et al. 2017). Coefficients of variation (CV) are given as percentages. The bold number is the CV used for our calculations. Average probability of detection assuming g(0)=1 Probability of detection on the transect line, g(0) Overall average probability of detection Species Estimate CV Estimate CV Estimate CV Common + striped dolphin 0.131 11.7 0.421 11.5 0.055 16.4 TABLE A5. Effort and sighting rates (all species) during STAR06, shown by sea state (Xxxxxxx et al. 2008). Beaufort Effort Sightings Sightings/1000km 0 100.1 33 329.55 1 375.4 78 207.8 2 1729.8 221 127.76 3 3212.2 261 81.25 4 9375.5 336 35.84 5 6952.1 199 28.62 6 492.1 7 14.23 7 0 0 0 Total 22237.3 1135 51.04
Table A4. 1 Analytical Categories and Codes Guiding the Empirical Analysis in Chapter Three Analytical Categories and Codes Code Description Example Structuration – Political Authority Transferred to the Eurogroup/EWG Political Competences and Tasks Interviewees identify explicit and substantial governance tasks that were transferred to the Eurogroup or EWG. ‘The Eurogroup and the EWG had the biggest impact in setting up the EFSF and ESM and then signing off the disbursements.’ (Interview 16) Standardisation – Norms and Values within the Eurogroup/EWG Organisational Norms Interviewees identify behavioural norms and expectations shared among Eurogroup or EWG members. ‘So, the expectation for an ideal colleague is to be a good economist. Don't chatter awake, be precise, know exactly what your minister is thinking[...], know exactly what the red lines are, explain the will and ideally have a polite understanding that your point of view is possibly correct from a national point of view, but that there is a high degree of legitimacy to the point of view of others who disagree with you.’ (Interview 17) Crisis Perception Interviewees indicate how they or others understood the main reasons of and solutions for the sovereign debt crisis. ‘The focus on fiscal rigour and budget restriction in the long term was counterproductive, frankly and I think we are learning it now. You know it is right to be sure that the country and especially mine, are rigorous and disciplined in the way you use public money. But I think Europe as a whole should have been much more concerned about the impact of the fiscal deficit on the area of growth potential.’ (Interview 23) Reallocation – Administrative Resources Allocated to the Eurogroup/EWG Resources of the Secretariats Interviewees report on administrative support that the Council Secretariat or European Commission dedicated to the Eurogroup or EWG. ‘On the Eurogroup: Yes, I'm sometimes tempted to think I'm the secretary, but I'm not. There is no secretary to the Eurogroup, but the secretariat function for the Eurogroup is split. I would still say 80% is done in my Secretariat, 20% is done in the General Secretariat of the Council, Xxxxxxx Xxxxxxx, Xxxx Xxxxxxxx, these people.’ (Interview 15) Eurogroup/EWG Chairs Interviewees reflect on to what extent the authority of and the administrative support to the chairs of the Eurogroup or EWG have been reformed. My role was much more centred on explaining to the media in Brussels and everybody in Bru...
Table A4. Propensity Score Matching Models Predicting the Effect of Recent Paternal Incarceration on Change in Father's Parenting Between the Three- and Five-Year Surveys Panel A. Residential Fathersa Change in shared Change in Change in responsibility in cooperation in Change in Treatment N Control N engagement parenting parenting parenting stress Nearest neighbor 133 1,746 -1.131 *** (0.244) -0.647 *** (0.135) -0.260 ** (0.092) -0.207 * (0.097) Radius 133 1,746 -1.129 *** -0.671 *** -0.275 ** -0.148 (0.232) (0.129) (0.087) (0.092) Kernel 146 1,746 -1.153 *** -0.679 *** -0.321 *** -0.111 (0.213) (0.122) (0.079) (0.085) Panel B. Nonresidential Fathers Change in shared Change in Change in responsibility in cooperation in Change in Treatment N Control N engagement parenting parenting parenting stress Nearest neighbor 470 1,179 -0.213 ^ (0.121) -0.129 * (0.065) -0.185 * (0.075) 0.225 * (0.094) Radius 470 1,179 -0.235 * (0.115) -0.129 * (0.063) -0.181 * (0.074) 0.219 * (0.092) Kernel 494 1,179 -0.287 ** -0.141 * -0.161 * 0.137 (0.107) (0.060) (0.070) (0.084) Note: Ns for parenting stress are smaller. For analyses of residential fathers, treatment N = 78, control N = 1,499 for nearest neighbor matching; treatment N = 78, control N = 1,499 for radius matching; treatment N = 91, control N = 1,499 for kernel matching. For analyses of nonresidential fathers, treatment N = 156, control N = 572 for nearest neighbor matching; treatment N = 156, control N = 572 for radius matching; treatment N = 170, control N = 572 for kernel matching. * p < 0.05, ** p < 0.01, *** p < 0.001. a Residential fathers include all fathers living with the mother and focal child at the three-year survey. Nonresidential fathers include all fathers not living with the mother and focal child at the three-year survey. Table A5. Descriptive Statistics of Biological and Social Fathers' Parenting at Five-Year Survey, by Biological Father Recent Incarceration Biological father recently incarcerated Biological father not recently incarcerated Biological father Social father Biological father Social father Engagement 0.478 3.654 *** 1.046 3.670 *** Shared responsibility in parenting 1.452 3.750 *** 1.674 3.583 *** Cooperation in parenting 2.705 2.814 2.738 2.861 N 26 26 36 36
