Analysis of the Proposed Implementation of the New Army Communications and Information Systems Specialist (ACISS) Trade using the Managed Readiness Simulator (MARS)
Bibliographic record
Abstract
Abstract : The Army is in the process of restructuring the Signals Non-Commissioned Members occupations to create a new Army Communication & Information Systems Specialist (ACISS) occupation by combining the current Land Communications and Information Systems Technician; Signal Operator; and Linemen occupations. In order to validate the complex ACISS structure concept and to optimize the implementation and sustainment of this new occupation, the Managed Readiness Simulator (MARS) program was used to conduct some population modelling analysis. The ACISS structure along with a proposed intake and training plan were successfully modelled in MARS for a 12 year period. Preliminary results indicated that it would take approximately nine years to fill most of the vacant positions in the ACISS structure due to bottlenecks in the training system indicating that the proposed training plan was inadequate to support the proposed intake plan. Proposals to address these bottlenecks were then examined along with proposed reductions to the intake plan to prevent trained recruits from being unable to find a job in the ACISS structure. The result of the proposed changes was an ACISS structure filled in approximately half the time of the initial training proposal and one that allowed virtually all new recruits to find a position in the ACISS structure. Finally, it was recommended that MARS be run both periodically and whenever changes are proposed to forecast potential issues with the ACISS occupation so that Canadian Forces (CF) management has the opportunity to address the issues in a timely manner with a better understanding of the potential risks.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".