Big Data Enabling Fish Farming Data-Driven Strategy
Bibliographic record
Abstract
The last two decades have witnessed an exponential data generation in tremendous amounts.The digital transformation in many domains leads to massive amounts of heterogeneous data.In order to benefit from this generation, value is extracted through data processing.Meanwhile, the fish farming functioning activity generates data with such volume, speed, heterogeneous sources and structures but it is not fully exploited.The traditional data warehouse solutions are not able to manage complex data with these characteristics.Thus, the concept of the data lake has emerged for more flexible and powerful data exploitation.Indeed, big data technologies and techniques are used to extract, process and analyze data.Since big data technologies have proved their benefits in other domains, it is irrefutable that using them in the fish farming domain will help it to reach its full potential.For this purpose, we propose in this paper a dedicated data lake architecture for handling fish farming data to initiate the adoption of a data driven strategy.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".