Bibliographic record
Abstract
This paper analyzes ”the image of snow” which appears in the poems of Luo-Fu, and discuses the expression in the time of memory, in the pass and future, and show to the meaning of emotions by Luo-Fu's poems. The first, the paper relates simply the relation between snow and Luo-Fu, and makes a description of aspects of this paper. The second, this paper explains the image of snow located by Luo-Fu's heart, and expresses the heart progress of Luo-Fu by the snow imagination in the poems that shows the motion insight by the poems. By this way, the paper would recall the ancients who have created the same snow imagination and the poet, Luo-Fu, wrote many poems symbolized and imaged the snow meanings and would express ingenious writings between each other. The third, the point is what's the changes in meaning discussion. The number one, in Taiwan, the snow recalls his homesickness by cherish of memory and poet's feeling trend. The number two, the poet still misses his native place, but had come back his home. The number three, the snow changes the poet's heart to a new metamorphosis and changes into the reverse when the poet has immigrated to Canada. The serene living and peace forwarded by snow, the poet has been trend to Buddhist. The forth, this is conclusion. This paper stands in the first poetry-works to 2007 printed, analyzes the meanings in snow images by linked of poet's heart and snow to which is the main meaning and feeling and to the poet whose feeling-condition that is matching the meaning of snow images and poet's feeling changed condition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".