MétaCan
Menu
Back to cohort
Record W6976771511 · doi:10.6068/dp15571bfc2e220

TREND: International Monetary Fund. Direction of Trade Statistics: Imports | Country: United Kingdom | Trading Partner: NETHERLANDS, 1980/1 - 2015/4. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 056-002-004

2016· other· en· W6976771511 on OpenAlexaboutno aff

Bibliographic record

VenueData Planet · 2016
Typeother
Languageen
FieldSocial Sciences
TopicHealth, Education, and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMandateCONQUESTPaymentValue (mathematics)Exchange rateBalance of paymentsQuarter (Canadian coin)Position (finance)

Abstract

fetched live from OpenAlex

International Monetary Fund. Direction of Trade Statistics: Imports | Country: United Kingdom | Trading Partner: NETHERLANDS, 1980/1 - 2015/4. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 056-002-004 Dataset: Shows "cost, insurance, freight" (c.i.f.) import value for each country, by trading partner. Cost, insurance, freight measures freight including cargo insurance and delivery of goods to the named port of destination (discharge) at the seller's expense. Buyer is responsible for the import customs clearance and other costs and risks. Provides statistics on the value of merchandise exports and imports disaggregated according to a country’s trading partners. Reported data are supplemented by estimates when such data are not available or current. http://data.imf.org/?sk=9D6028D4-F14A-464C-A2F2-59B2CD424B85 Category: International Relations and Trade Subject: Customs, International Trade, Imports Source: International Monetary Fund Headquartered in Washington, DC, the International Monetary Fund (IMF) was conceived at a United Nations conference convened in Bretton Woods, New Hampshire, United States, in July 1944. The 44 governments represented at that conference sought to build a framework for economic cooperation that would avoid a repetition of the vicious circle of competitive devaluations that had contributed to the Great Depression of the 1930s. As of 2015, the IMF has 188 member countries. Its primary purpose is to ensure the stability of the international monetary system, specifically the system of exchange rates and international payments that enables countries (and their citizens) to transact with one other. This system is essential for promoting sustainable economic growth, increasing living standards, and reducing poverty. The Fund’s mandate has recently been clarified and updated to cover the full range of macroeconomic and financial sector issues that bear on global stability. The IMF is a specialized independent agency of the United Nations but has its own charter, governing structure, and finances. Its members are represented through a quota system broadly based on their relative size in the global economy. http://www.imf.org/

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.357
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueData PlanetSame topicHealth, Education, and Cultural StudiesFrench-language works237,207