Predictive Analysis of the Ornamental Angelfish Export Market Demand: An Application of the Least Square Method
Bibliographic record
Abstract
Fluctuations in the stock levels of the ornamental angelfish export market have highlighted the necessity for an effective demand prediction system.In response to this need, the present study undertakes the development of a demand prediction model, employing the Least Square Method, for the ornamental angelfish market.The model is evaluated using a dataset comprising 4166 individual records across three ornamental angelfish samples from the year 2021.The model's predictive accuracy is quantitatively assessed through the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) compared to actual data.The results indicate a high level of accuracy, with an average MSE value of 39, an average MAD value of 5, and an average MAPE value of 5%.This study's findings contribute valuable insights to the ornamental angelfish export industry, demonstrating the efficacy of the Least Square Method in forecasting demand, and propose potential trajectories for further research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".