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Record W7098612576

Effects of Long-term Cultivation on a Morainal Landscape

2015· article· en· W7098612576 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterAgricultureSoil qualityCroppingBenchmark (surveying)Soil mapSoil retrogression and degradationLand useHydrology (agriculture)
DOInot available

Abstract

fetched live from OpenAlex

Agriculture and Agri-Food Canada established a benchmark site network in the early 1990s to monitor soil quality change over time on reference agricultural landscapes. This approach assumes that monitoring selected soil variables for 10 or more years will show changes in soil conditions. An alternative approach is to compare cultivated and uncultivated (native) soils in a landscape. This approach requires less research time and provides an estimate of changes in soil characteristics over the entire cultivation period. Opportunities for such research are rare in the agricultural parts of western Canada. An opportunity presented itself in conjunction with work at the Provost (05-AB) benchmark site. A parcel of native land only 1.6 km away was studied and sampled using the same methodology. The similarity of the two sites provided an opportunity to examine soil attributes that approximated conditions prior to cultivation, and, by comparison, to assess changes brought about by 80 years of cultivation. Literature Review The impact of soil erosion on soil quality has been investigated using long-term cropping system studies1,2,7 and retrospective views of management-induced soil changes by comparing cultivated with uncultivated soils in the same landscape6,7. Differences in organic

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.058

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 head0.032
GPT teacher head0.217
Teacher spread0.185 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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