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Record W7095850378

State Governing Board

2015· article· en· W7095850378 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsnot available
Fundersnot available
KeywordsShut downMineral resource classificationNatural gasPipeline transportRaw materialPetroleum industryPumicePetroleum
DOInot available

Abstract

fetched live from OpenAlex

Oregon's mineral industry produced its second highest value of row minerals in 1960. Following a nation-wide economic trend during the year, the industry was off approximately $3 million from lost year's record-breaking high of $49.8 million, according to preliminary estimates mode by the U. S. Bureau of Mines. The heavy construction commodities, crushed stone and sand and grovel, reflected construction log and were responsible for most of the change from lost year. Metal mining, aside from nickel, was quiet. The state's only uronium mine and one of the two mercury producers shut down. Industrial mineral products showed both gains and losses as compared to the previous year. Cement production was up 12 percent while clays declined 15 percent and diatomite 3 percent. Pumice and volcanic cinders were up 9 percent and stone and s(;md and gravel were off 11 percent. Building stone activity was greatly increased over 1959. Two major tests for oil were conducted during the year. Construction of a natural gas pipeline from Camas, Washington, to Eugene, Oregon, provided the area with an important raw material basic to many industrial operations. A second pipeline extending from Alberta, Canada, to California was started late in the year. The line will make natural gas available to such points as Bend, Klamath Falls, Medford, and the Rogue River Valley. Pacific Power and Light Company conducted by-product tests

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.024
GPT teacher head0.211
Teacher spread0.187 · 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
GenreEmpirical

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
Published2015
Admission routes1
Has abstractyes

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