Bibliographic record
Abstract
Attending the Canadian Institute of Forestry/Institute forestier du Canada (CIF/IFC) / Society of American Foresters (SAF) Joint Convention in Edmonton Alberta, Canada was an incredible experience.I would like to thank the CIF/IFC and SAF administration and the many volunteers who made the event possible, and commend them on a job well done.I would also like to thank the Mississippi Chapter of the Society of American Foresters and Mississippi State University (MSU) for sponsoring the 14 MSU students who attended the convention.For a little background information about myself, I am a senior in forestry at Mississippi State University.After graduation I plan to continue my educational studies by attending graduate school at Mississippi State, focusing on an aspect of forestry and wildlife interactions.Conventions of this nature offer many opportunities for both students and professionals.They are fun and entertaining as well as educational and provide a great opportunity for students to travel to places they have never been.In my three years of involvement with SAF, I have traveled to Quebec City, Quebec; Buffalo, New York; Edmonton, Alberta; Nebraska City, Nebraska; and Clemson, South Carolina.Without being involved in SAF I might never have had the opportunity to visit these places.While in Edmonton some of our students took a trip to the Jasper National Park where they saw moose and mountain goats in their natural habitat as well as flora that differs greatly from what we are accustomed to in Mississippi.The impressive Rockies offered unfamiliar species such as Aspen, Larch, and Spruce.We have been taught a lot about the boreal region but seeing it first hand really brought it all together.Edmonton is also a fun city.The West Edmonton Mall is like nothing I have ever seen before, and the variety of restaurants and pubs was impressive.The convention planners did a great job organizing social events for students as well as general participants.Students were given the opportunity to compete in the Quiz Bowl, which is a forestry knowledge game where school teams compete against each other.Questions are asked from topics such as fire, dendrology, agroforestry, silviculture and biometrics.This was CIF/IFC's first year to host a Quiz Bowl and they did an outstanding job.As students, we had the opportunity to meet and network with not only forestry students and professionals from other schools but from other countries as well.I learned a lot from other students just through casual conversation about differences in the composition of flora, rotation length, harvesting methods, and management practices.These discussions once again brought book knowledge into reality.Hearing the information from a peer's personal experience and discussing it develops a better understanding of the subject, and it is knowledge that you will more than likely retain.Although there are many differences throughout the forestry profession, one aspect of it remains the same, a general sense of comraderie.We had many opportunities to meet and talk with professionals in forestry and related fields.The convention's atmosphere encouraged networking between students and professionals not only because of the relaxed, comfortable atmosphere, but also because students were treated as professionals, which built confidence.Conventions are also great places for students to learn of job opportunities.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 0.019 |
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".