ANALISIS KEPUASAN IMPORTIR BUAH (APEL,PIR DAN JERUK) \nTERHADAP PELAYANAN PT. TERMINAL PETIKEMAS SURABAYA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
International trade is generally carried out through the port because it is \nalways in large numbers to supply the people needed. Tanjung Perak Harbor is the \nlargest port in East Java, managed by PT. Terminal Petikemas Surabaya. The highest \nvolume of fruit imports are apples, oranges and pears from various countries such as \nChina, the United States, and Canada. Services of PT. Terminal Petikemas Surabaya \nhas a big effect on service users or consumers, in this case is the fruit importer. The \ntime proceed to entry of commodities at the port affects the stability of prices at the \nconsumer level. \nThe purpose of this study was to determine the satisfaction of importers with \nthe services of PT. Surabaya Container Terminal and analyzed the effect of service \nquality on the satisfaction of importers of apples, oranges and pears. There are 50 \ncompanies selected using the Purposive Random Sampling method. While the \nanalysis uses Multiple Linear Regression. \nThe results showed that there were 4 (four factors) which had a significant \neffect on importer satisfaction, Such as responsiveness, empathy, assurance and \nphysical evidence. The reliability factor does not significantly influence the \nsatisfaction of the importer. All the 5 (five) independent variables that affect 68.5% of \nimporter satisfaction. As many as 31.5% of other factors were not included in this \nstudy.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it