Bibliographic record
Abstract
The goal of developing Costless Node Network (CNN) is to engineer a way to extract information from a farm in an effortless and productive manner. The purpose of CNN stems from the laborious task of manually and visually judging and collecting data and how this leads to inefficiencies and poor and limited access to data as the size of the farm increases. Collected data is intuitively presented to the farmer to enhance their crops. The type of data, volume of data, speed of access, will be the deliverables for this project.\nThe goal of CNN is to minimize physical efforts to collect data with initial capital cost  and maximize productivity for the farmers. This product is a comprehensive solution to all the challenges faced by farmers across the globe. Farmers suffer from low yielding  and increasing costs to the point that most barely breakeven. CNN’s primary goals is to reverse this experience by providing high resolution data allowing the farmer to take control of the crops and to expect healthy yields year after year. Using CNN means deploying a method which leads to a very healthy crop yield at a very low costs. Furthermore, CNN means a significant reduction in tedious labour.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".