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Record W2059237287 · doi:10.1080/00288233.2003.9513539

A cost‐benefit analysis of the value of investment in a wapiti hybridisation research programme and the returns to New Zealand venison producers

2003· article· en· W2059237287 on OpenAlexaboutno aff
G. H. Shackell, K. R. Drew, A. J. Pearse, P.R. Amer

Bibliographic record

VenueNew Zealand Journal of Agricultural Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsInternal rate of returnPurebredInvestment (military)HybridBiologyHerdNet present valueRate of returnValue (mathematics)Agricultural scienceAgricultural economicsEconomicsEcologyBreedAgronomyStatisticsProduction (economics)Mathematics

Abstract

fetched live from OpenAlex

Abstract Between the mid 1970s and the end of the 1980s a research programme evaluated hybridisation of red deer, initially with wapiti‐type captured animals and then with purebred wapiti imported from Canada. The cost of the research programme in 2001 dollars was $7.2 million. By 1980, the deer industry had begun introducing wapiti genes into farmed red deer herds. In 1990 wapiti hybrids accounted for approximately 5% of deer slaughtered for venison in New Zealand. By 2001 that figure had risen, possibly to as high as 50%. Due to their rapid growth to target slaughter weights, wapiti‐red deer hybrids have a greater potential than red deer of the same age to reach suitable weights in time to attain early season price premiums. Furthermore, the liveweight advantage of hybrid weaners over red deer has historically given them a price advantage at time of sale. Wapiti hybrids contributed a net present value (NPV) at 7% discount rate of $10.3 million to the New Zealand venison industry up to 2001 (internal rate of return (IRR) 13.4%). Based on MAFPolicy projections of numbers of animals slaughtered, the NPV (7%) is expected to increase to $23.6 million by 2006 (IRR 17.6%).

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.040
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.327
Teacher spread0.265 · 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.

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

Citations5
Published2003
Admission routes1
Has abstractyes

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