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Record W2602370409

Milk Cost of Production Estimates for July, August, and September 2016

2016· preprint· en· W2602370409 on OpenAlexaboutno aff
John Bovay

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Agricultural economicsProduction (economics)PaymentProduction costBusinessEconomicsAgricultural scienceFinanceGeographyEnvironmental scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

Milk prices for Connecticut farmers rebounded to $16.66/cwt in the third quarter of 2016, the highest monthly average price since the fourth quarter of 2015. A dumping policy available to producers under the Northeast region’s federal milk marketing order helped somewhat with these prices during the first half of the quarter. At the same time, the monthly average cost of production increased by $2.24/cwt, to $34.25/cwt. Over 40% of the increase in cost of production ($0.93/cwt) can be attributed to an increase in the cost of purchased feed per hundredweight of milk. The monthly average shortfall of prices minus the minimum sustainable cost was $11.42/cwt, similar to the shortfall in the second quarter. Thus, we see a need for continuing payments in the future to Connecticut dairy farmers under Public Act 09-229. Looking ahead, national milk prices are expected to remain low; national milk production in August was 1.9 percent higher than in August 2015. Feed prices are expected to remain fairly stable. Taking these factors together, we can expect that the minimum sustainable cost of milk production will continue to exceed the price of milk, and that Connecticut dairy farmers would face additional financial pressure in the absence of payments under Public Act 09-229. Length: 1 page

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.251
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.040
GPT teacher head0.288
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreOther

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

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Same venueRePEc: Research Papers in EconomicsSame topicEconomics of Agriculture and Food MarketsFrench-language works237,207