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Record W1964112337 · doi:10.4141/p06-103

Fresh market sweet corn production with clear and wavelength selective soil mulch films

2007· article· en· W1964112337 on OpenAlex
T. Q. Zhang, C. S. Tan, J. Warner

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.

venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsnot available
Fundersnot available
KeywordsMulchAgronomyLoamWater contentEnvironmental scienceMoistureSoil waterChemistryBiologySoil scienceGeology

Abstract

fetched live from OpenAlex

Earliness of fresh market sweet corn (Zea mays L.) is important to increasing profitability and maintaining market occupancy. Maturity of fresh market sweet corn may be advanced by the use of plastic soil mulch films. In 2000 and 2001, the effects of clear (CMF) and wavelength selective (WLSMF) mulch films on soil temperature and moisture and the performance of fresh market sweet corn with and without N fertilization were evaluated in a Granby loamy sand soil in southwest Ontario. Both mulch films increased soil temperature and moisture compared with bare soil. Soil temperatures were 1.8°C higher at 5 cm and 1.6°C higher at 15 cm soil depth under CMF than WLSMF averaged over the growing season in two years. Both mulches increased soil moisture levels relative to bare soil, but less increase occurred under CMF than WLSMF. Both CMF and WLSMF advanced sweet corn maturity by 6-7 d relative to the bare soil. Compared with bare soil, marketable yields increased by 25 to 63% without added N and by 72 to 114% with added N under CMF. Under WLSMF, the corresponding increases in marketable yields were 97 to 98% without added N and 120 to 200% with added N. While WLSMF was superior to CMF for increasing fresh market sweet corn yields in southwestern Ontario, the relative economic advantage of each mulch type needs to be studied. Key words: Marketable yield, nitrogen, soil cover, soil moisture, soil temperature

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)0.001
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 score0.551
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.191
Teacher spread0.179 · 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