Geographic information systems and site selection issues of open sea cage culture
Bibliographic record
Abstract
As is much known in the Information Technology circles, \na pair of numbers narrates the past, describe the present \nand in fact most importantly seal the future. The pair \nobviously means the latitude and longitude of the location \nany where under the sky. This perspective of referencing \nany type of information be it scientific, sociological, \npsephological or economic, has taken the world of \nanalytics by storm in past quarter of a century. The last \ndecades of the previous millennium were dotted with \nspurt in methodologies and software which were totally \ndependent on this type of geo-referenced data. \nInformation collected serially over time, popularly known \nas time series, always had a huge role to play in studying \nthe impact of changing eras and centuries at larger level \nand seasons and cycles in shorter duration.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".