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Record W2019165659 · doi:10.1080/07060660409507142

Climate change and crop production: contributions, impacts, and adaptations

2004· article· en· W2019165659 on OpenAlexaffvenueabout
Donald L. Smith, Juan J. Almaraz

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsMcGill University
Fundersnot available
KeywordsGreenhouse gasEnvironmental scienceClimate changeAgricultureCroppingProduction (economics)Land use, land-use change and forestryAgronomyAgroforestryNatural resource economicsEcologyEconomicsBiology

Abstract

fetched live from OpenAlex

Crop production and climate change affect each other because crop production (1) produces greenhouse gases (GHGs), (2) is affected by climate change, (3) will have to adapt to changed climatic regimes, and (4) has a potential role in mitigating the production of GHGs. Agriculture is not a major producer of GHGs, at less than 10% of Canada's total. Agriculture is a major producer of methane and nitrous oxide (21 and 310 times more effective at heat trapping than CO2, respectively), but a minor producer of CO2. The impacts on agriculture will come through increased CO2 effects on plant growth, warmer and drier conditions, changes in wind speed, insect and disease pressures, and many more subtle changes resulting from altered interactions among components of crop agro-ecosystems. Predictions are for net increases in world food production as temperature increases become larger. Potential adaptations are (1) management and genetic alterations to crops, (2) legislative changes, (3) policy and economic changes, and (4) adoption of mitigation practices. Mitigation of GHG effects can be through new cropping systems and crops that reduce net GHG production by emitting less nitrous oxide, increasing soil organic matter content, and allowing production of bio-products such as bio-fuels.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.954

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.214
Teacher spread0.188 · 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

Citations41
Published2004
Admission routes3
Has abstractyes

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