Montana Conservation Corps Intern
Bibliographic record
Abstract
During my internship with the Montana Conservation Corps (MCC) and the Absaroka-Beartooth Wilderness Foundation (ABWF), I was originally placed with the Gardiner Ranger District trail crew. However, due to Forest Service budget cuts, I had the opportunity to engage with a wide range of departments, including recreation, wildlife, botany, and silviculture. This cross-departmental exposure allowed me to fulfill key learning objectives such as gaining hands-on skills in trail maintenance, understanding federal conservation work, and developing ecological restoration competencies. A significant portion of my internship involved trail maintenance within designated wilderness areas, where I became certified in crosscut saw use and practiced safe and effective techniques for felling and bucking trees. I also worked extensively with traditional tools like Pulaskis and axes, and learned to care for and utilize pack animals, such as horses and mules, in wilderness trail work. In addition to trail work, I contributed to several Forest Service conservation projects. These included a goshawk nesting survey, an old-growth forest stand assessment, and a Canada lynx habitat analysis—each tied to a proposed timber sale and aligned with environmental review procedures under the National Environmental Policy Act (NEPA). These projects provided insight into how federal land management balances ecological stewardship with resource-based decision-making. Through this internship, I maintained 30 miles of trail, improved 10 public recreation sites, and assisted in protecting habitat for multiple sensitive species. I not only exceeded my learning objectives but also gained clarity on potential career paths within trail work, ecological research, and federal land management. This experience deepened my understanding of wilderness stewardship and strengthened my desire to pursue a career in natural resources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.488 | 0.141 |
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".