Heuristic methods for designing a global positioning system surveying network in the Republic of Seychelles
Bibliographic record
Abstract
سإ يب يجلا ةيعانصلا رامقلأاب ةدوصرملا ةيحاسملا تاكبشلا ءاشنإ ةبوعص دادزت (GPS) دايدزا عم ةبوعصلا ةغلاب لاـ عفلا لحلا ىلع ةينبملا اهميمصت ةيلمع حبصت يلاتلابو اهمجح . يب يجلا ةكبش د دحت ةينمزلا تاسايقلا وأ تاراشلاا دصرب ةيحاسملا سإ (session) لا ةيوستلا طاقن نيب ةلكشتم (station) لابقتسلاا ةزهجأ ةطساوب (receiver) طاقنلا هذه ىلع ةعضومتملا . نع ثحبلا ةيفيآ ةلاقملا هذه نيبت ةنكمم ةلودـج لضفأ ىلع لوصحلا فدهب تاراشلإا هذه دصرل لضفلأا لسلستلا (schedule) . مت ةيبيرقتلا ةينيسحتلا قرطلا مادختسا (Heuristic methods) ميمصتل ةلاـ عف ةيبوساح جمارب ىلع ةينبملا تاكبشلا هذهل ةيلاثملا نم ةبيرقلا وأ ةيلاثملا لولحلا نيمأتلو ةريبكلا ةيحاسملا سإ يب يجلا تاكبش . امآ ةدمتعملا ةيبيرقتلا ةينيسحتلا قرطلا ءادأو ةيلاعف راهظلإ ةصلاخلا جئاتنلاو ةيباسحلا تايلمعلا ضرع مت امهو : يبيرجتلا نيدلتلا ةقيرط (simulated annealing) روظحملا ثحبلا ةقيرطو (tabu search) . لولحلا ةدوج نيب ةنراقملا ءارجلإ كلذو ةيحاسملا ةكبشلا سفن ىلع نيتقيرطلا نيتاه قيبطت مت دقل لولحلا هذه ىلع لوصحلل ةيباسحلا ةعرسلاو ةجتانلا . ةساردلا هذه يف تمدختسا ) تامولعمآ ةيسايق ( سملا سإ يب يجلا ةكبش دصرل ةيلعفلا لمعلا ةطخ لشيس ةيروهمج يف ة ذـفنملا ةيحا . HEURISTIC METHODS FOR DESIGNING A GLOBAL POSITIONING SYSTEM SURVEYING NETWORK IN THE REPUBLIC OF SEYCHELLES
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".