- We are watching this very closely, and as the questioner perhaps knows, proposal 90/220 contains some provisions which mean that GMO products will not simply be approved for all time, but will be subject to reassessment, because we cannot rule out gaining some experience of the long-term effects or obtaining some new information.
- Detta följer vi mycket noga, och som frågeställaren kanske vet innehåller ju förslag 90/220 vissa bestämmelser om att de genmodifierade organismerna inte är godkända för tid och evighet, utan återigen skall tas upp till bedömning, eftersom man inte kan utesluta att vi får vissa erfarenheter med tiden, eller att vi får ny information.
- The importance of new information and communication technologies (ICTs) can be attributed to their crucial role in starting a veritable revolution in the world of science, not only by proclaiming the birth of the knowledge-based society, but also by making a sustainable approach to the use of natural resources possible.
- Den nya informations- och kommunikationstekniken (IKT) har haft en avgörande roll för den fullständiga revolution som inletts på vetenskapsområdet, inte endast genom att bana väg för det kunskapsbaserade samhället, utan även genom att möjliggöra en hållbar inställning när det gäller utnyttjandet av naturresurserna.
show query
SET search_path TO f9miniensv;
WITH
list AS (SELECT
t11.token_id AS t11,
t12.token_id AS t12,
t21.token_id AS t21,
t22.token_id AS t22,
r1.dep_id AS dep1,
r2.dep_id AS dep2
FROM
deprel r1
JOIN depstr s1 ON s1.dep_id = r1.dep_id
JOIN word_align a1 ON a1.wsource = r1.head AND a1.wsource < a1.wtarget
JOIN word_align a2 ON a2.wsource = r1.dependent
JOIN deprel r2 ON r2.head = a1.wtarget AND r2.dependent = a2.wtarget
JOIN depstr s2 ON s2.dep_id = r2.dep_id
JOIN token t11 ON t11.token_id = r1.head
JOIN token t21 ON t21.token_id = r2.head
JOIN token t12 ON t12.token_id = r1.dependent
JOIN token t22 ON t22.token_id = r2.dependent
WHERE
s1.val = 'amod' AND
s2.val = 'AT' AND
t11.ctag = 'NOUN' AND
t21.ctag = 'NOUN' AND
t12.ctag = 'ADJ' AND
t22.ctag = 'ADJ' AND
t11.lemma_id = 1295 AND
t12.lemma_id = 15618 AND
t21.lemma_id = 1295 AND
t22.lemma_id = 53462),
stats AS (SELECT
sentence_id,
count(DISTINCT token_id) AS c,
count(*) AS c_aligned,
count(DISTINCT wtarget) AS c_target
FROM
token
LEFT JOIN word_align ON wsource = token_id
WHERE
sentence_id IN (
SELECT sentence_id
FROM
list
JOIN token ON token_id IN(t11, t21)
)
GROUP BY sentence_id),
numbered AS (SELECT row_number() OVER () AS i, *
FROM
list),
sentences AS (SELECT *, .2 * (1 / (1 + exp(max(c) OVER (PARTITION BY i) - min(c) OVER (PARTITION BY i)))) +
.8 * (1 / log(avg(c) OVER (PARTITION BY i))) AS w
FROM
(
SELECT i, 1 AS n, sentence_id, ARRAY[t11,t12] AS tokens
FROM
numbered
JOIN token ON token_id = t11
UNION SELECT i, 2 AS n, sentence_id, ARRAY[t21,t22] AS tokens
FROM
numbered
JOIN token ON token_id = t21
) x
JOIN stats USING (sentence_id)
ORDER BY i, n)
SELECT
i,
n,
w,
c,
c_aligned,
c_target,
sentence_id,
string_agg(CASE WHEN lpad THEN ' ' ELSE '' END || '<span class="token' ||
CASE WHEN ARRAY[token_id] <@ tokens THEN ' hl' ELSE '' END || '">' || val || '</span>',
'' ORDER BY token_id ASC) AS s
FROM
sentences
JOIN token USING (sentence_id)
JOIN typestr USING (type_id)
GROUP BY i, n, w, c, c_aligned, c_target, sentence_id
ORDER BY w DESC, i, n;
;