An interactive graph-based automation assistant
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
The GIPSY system provides a framework for a distributed multi-tier demand-driven evaluation of heterogeneous programs, in which certain tiers can generate demands, while others can respond to demands to work on them. They are connected through a virtual network that can be flexibly reconfigured at run-time. Although the demand generator components were originally designed specifically for the eductive (demand-driven) evaluation of Lucid intensional programs, the GIPSY's run-time's flexible framework design enables it to perform the execution of various kinds of programs that can be evaluated using the demand-driven computational model. Management of the GISPY networks has become a tedious (although scripted) task that required a manual command-line console, which does not scale for large experiments. Therefore a new component has been designed and developed to allow users to represent, visualize, and interactively create, configure and seamlessly manage such a network as a graph. Consequently, this work presents a Graphical Manager, an interactive graph-based assistant component for the GIPSY network creation and configuration management. Besides allowing the management of the nodes and tiers (mapped to hosts where store, workers, and generators reside), it lets the user to visually control the network parameters and the interconnection between computational nodes at run-time. In this paper we motivate and present the key features of this newly implemented graph-based component. We give the graph representation details, mapping of the graph nodes to tiers, tier groups, and specific commands. We provide the requirements and design specification of the tool and its implementation. Then we detail and discuss some experimental results.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it