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Record W3121677831

The Pricing Performance of Market Advisory Services in Cattle Over 1995-2004

2012· article· en· W3121677831 on OpenAlexaboutno aff
Tracy L. Brandenberger, Scott H. Irwin, Darrel Good

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)FeedlotFeeder cattleWindow of opportunityMargin (machine learning)Futures contractBusinessMarketingAgricultural economicsEconomicsAgricultural scienceEngineeringFinanceGeographyComputer scienceEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this report is to evaluate the pricing performance of market advisory services’ live cattle hedging recommendations over 1995-2004. Also, feeder cattle, corn, and soybean meal recommendations were evaluated as input hedges and combined with the live cattle marketing recommendations to approximate the margin that a typical feedlot would face from the third quarter of 1999 through 2004. Other marketing assumptions were also applied to approximate a real world feedlot in Western Kansas. Several key assumptions are 1) the feedlot markets on average 1 cwt. of live cattle per quarter, inputs are purchased at rates that will yield on average 1 cwt. of live cattle per quarter, or 4 cwt. total per year, 2) the marketing widow for live cattle marketings begins six months prior to the start of the marketing quarter, making the total marketing window nine months long, 3) brokerage costs are subtracted from futures and options markets gains or losses and 4) the quarterly purchases of inputs, live cattle marketings and benchmark prices are weighted by quarter to reflect the cyclical nature of live cattle marketing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.182
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2012
Admission routes1
Has abstractyes

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