MétaCan
Menu
Back to cohort
Record W2766390322 · doi:10.1111/pce.13090

Molecular improvement of alfalfa for enhanced productivity and adaptability in a changing environment

2017· review· en· W2766390322 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePlant Cell & Environment · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsAdaptabilityMedicago sativaAgricultureProductivityAgronomyForageBiologyPopulationFood securityAgroforestryEcologyEconomics

Abstract

fetched live from OpenAlex

Due to an expanding world population and increased buying power, the demand for ruminant products such as meat and milk is expected to grow substantially in coming years, and high levels of forage crop production will therefore be a necessity. Unfortunately, urbanization of agricultural land, intensive agricultural practices, and climate change are all predicted to limit crop production in the future, which means that the development of forage cultivars with improved productivity and adaptability will be essential. Because alfalfa (Medicago sativa L.) is one of the most widely cultivated perennial forage crops, it has been the target of much research in this field. In this review, we discuss progress that has been made towards the improvement of productivity, abiotic stress tolerance, and nutrient-use efficiency, as well as disease and pest resistance, in alfalfa using biotechnological techniques. Furthermore, we consider possible future priorities and avenues for attaining further enhancements in this crop as a means of contributing to the realization of food security in a changing environment.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.040
GPT teacher head0.241
Teacher spread0.201 · 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