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Record W7112533952

Establishment of Early Winter Seeding of Forages in Cold Regions of Japan

2025· article· W7112533952 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2025
Typearticle
Language
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSeedingForageGrasslandSpring (device)PrecipitationGermination
DOInot available

Abstract

fetched live from OpenAlex

Eastern Hokkaido is located in the southern end of the sub­frigid zone. It has a very cold winter, although the summer is reasonably warm and humid. The annual temperature ranges between -25 °C and 30 °C, averaging about 7 °C in Obihiro. The precipitation averages about 900 mm per year. Soil freez­ing is very common in this area. Therefore, there is general agreement among workers that forage seeding in eastern Hokkaido must be confined to spring or summer. But the success of seeding is highly dependent upon the moisture con­tent of the soil. For example, in some years in eastern Hokkaido, grassland cannot be successfully established due to drought damage by ,< Tokachiharukaze >> a local name for special winds in the Tokachi Plain experienced during spring time. Hence we were very interested in studying the effect oflate fall (dormant) seeding (commonly practised in Canada) that ensures germination early in the following spring, so that young seedlings can take advantage of moisture from the win­ter snowfalls. The objective of this study was to determine the effect of seeding date on the establishment and survival of forage grasses and legumes as compared to traditional spring sown plots, and to compare the early growth of two grass species that were sown in early winter and spring (Anderson and Elliot, 1957; Brooke and Holl, 1988).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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