Exploring Command and Control Concepts for an Integrated Effect Coordination Cell using an Enhanced Tabletop Experimentation Approach: Report on the Integrated Effects Coordination Cell Exploratory Experiment (IECCEX)
Bibliographic record
Abstract
The first exploratory experiment of the Joint Fire Support (JFS) Technology Demonstration Project (TDP), namely the Integrated Effects Coordination Cell Exploratory Experiment (IECCEX), was conducted by the Canadian Forces Experimentation Center (CFEC) Effective Engagement Team (EET) and JFS TDP in the Joint Concept Laboratory and Training Center (JCLTC) located in CFEC, from 14th to 18th May 2007. The main objective of this experiment was to explore various Integrated Effects Coordination Cell (IECC) manning, process flow and control models in order to facilitate the identification of an appropriate model that best suits the requirements of providing optimal Integrated Effects (IE). In addition to the overall objective the scientific staff also gained valuable insight into: information requirements for each option; situational awareness requirements for each option; and areas best suited for automation. This report documents the approach used to conduct this experiment and captures the findings and lessons learned.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".