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Record W2053691919 · doi:10.1300/j411v11n01_01

New Dimensions in Agroecology for Developing a Biological Approach to Crop Production

2004· article· en· W2053691919 on OpenAlexaff
David R. Cléments, Anil Shrestha

Bibliographic record

VenueJournal of Crop Improvement · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsTrinity Western UniversityWestern University
Fundersnot available
KeywordsAgroecologyAgroecosystemSustainabilityAgricultureSustainable agricultureAgroforestryEcologyEnvironmental resource managementGeographyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Summary Agroecology is emerging from the conceptual realm to become a significant discipline in North America and many parts of the world. We explore 10 dimensions of agroecology that are important in developing a more biologically-based science of agriculture: (1) a new philosophy of agriculture, (2) systems thinking, (3) local adaptation, (4) the non-crop biota, (5) crop autecology, (6) encompassing the agricultural landscape, (7) closing the materials cycle: crops, livestock and local or global cycling, (8) technology and ecology, (9) human ecology, and (10) the natural dimension. Agroecology deals with the applications of ecological principles in agroecosystems and it represents a logical response to shortcomings of conventional agriculture. Current crop production approaches fail to account for biological complexities of agro-ecosytems and the need to feed the world without jeopardizing the sustainability of its life support systems. A key strategy employed by agroecologists is to compare agroecosystems and natural ecosystems systematically, and attempt to integrate knowledge of natural ecosystems into agricultural practice. Through this process, traditional agronomy is elevated to agroecology.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.219

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 head0.033
GPT teacher head0.248
Teacher spread0.216 · 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 designBench or experimental
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

Citations17
Published2004
Admission routes1
Has abstractyes

Explore more

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