ENT 530 Social Media Portfolio : Kelopak Bunga.MY / Siti Sarah Alyasa Gan
Bibliographic record
Abstract
Flowers are called the bloom or blossom of a plant. Flowers are often use as an indication of joy, happiness, sadness and sorrow too. Back then, flowers were used to signal meanings in the time when social meetings between men and women was difficult. Lilies made people think of life. Red roses made people think of love, beauty, and passion. In Britain, Australia and Canada, poppies are worn on special holidays as a mark of respect for those who served and died in wars. Daisies made people think of children and innocence. I believe that with flowers, we can show our emotions and feelings without having to say them. Due to this, I have decided on selling fresh flower hand bouquets for the ENT530 individual project where we are required to come up with our own product, as entrepreneurs and sell them to gain profit. The process involved several methods but was mainly focusing on the interactions of our social media which is Facebook. Requirements consist of posting teasers, softsell and hardsell on our very own page to gain followers and most importantly, customers. I have also registered my company legally on Syarikat Suruhanjaya Malaysia (SSM) in order to prevent consumers from feeling as if they fall as prey of cheats.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.770 | 0.687 |
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".