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Record W1988385320 · doi:10.4141/p06-078

White mulch and a south facing position favour strawberry growth and quality in high latitude tunnel cultivation

2007· article· en· W1988385320 on OpenAlexvenueno aff
Saila Karhu, R. Puranen, Abbas Aflatuni

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsnot available
Fundersnot available
KeywordsMulchFragariaHorticultureAgronomyPlastic mulchGrowing seasonEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Polyethylene mulches with black or white surface were compared in seven strawberry (Fragaria × ananassa Duch.) genotypes in a 2-yr experiment. The plants were covered by an unheated walk-in tunnel at the beginning of the first harvest season. The effects of a south-west versus north-east position of plants within the double-row beds were also studied. Soil temperatures were higher under black mulch, especially mid-day temperatures on the bed surface in the southward position. White mulch favoured root and crown growth in the first year, and the southwest position increased plant growth in both years. With black mulch, lower carbohydrate reserves were observed after transplanting, and a decreased chlorophyll content of leaves was detected in the second year. Mulch colour did not affect yield quantity. The first-year harvest was advanced with black mulch, but in the second year, the harvest season was earliest on the south side of beds with white mulch. Black mulch decreased fruit size in the first year and the concentration of fruit soluble solids in both years. The results suggest that in northern latitudes white mulch improves strawberry plant growth and fruit quality over that obtained using black mulch, but enhanced yield is not to be expected when a double-row tunnel cultivation system is used. Key words: Fragaria × ananassa, mulch, plasticulture, polyethylene, strawberry, tunnel

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)0.001
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 score0.265
Threshold uncertainty score0.999

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 head0.035
GPT teacher head0.253
Teacher spread0.218 · 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

Citations11
Published2007
Admission routes1
Has abstractyes

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