<i>Crop, Forage & Turfgrass Management</i> Annual Report: 2022
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
Crop, Forage and Turfgrass Management (CFTM) finished 2022 with more submissions (97) than 2021 (86).More than 70% are from the U.S. with Canada, Iran, Ethiopia, and Russia each contributing 4 or 5 submissions.One of the things we'd like to look at moving forward is increasing international submissions.For example, China was responsible the second most downloads of CFTM content in 2022 after the U.S., but we have not received a submission from China yet.In 2022, half of the articles we published were open access (26), and of these, 9 were funded by transformational agreements.These agreements cover the cost of publishing in our journal open access.The institutions, consortia, and countries covered under these agreements would be a good place to focus our marketing efforts to potential authors.The editorial board of the journal continues to shift to its efforts to commissioning more articles.We've made a concerted effort on increasing Review articles and plan to increasing the number of management guides, diagnostic guides, and web-based decision tools.Editorial board members traveling to various research conferences are provided with marketing materials to network with other researchers and showcase CFTM as a potential outlet for their work.There are myriad of opportunities for our journal to raise our profile within the research community.One way to do this is through special sections and virtual issues.Last year, we contributed an article to a virtual issue spanning many ASA, CSSA, and SSSA journals that will be published in 2023 under the title "Advancing Resilient Agricultural Systems: Adapting to and Mitigating Climate Change."In 2023, we hope to initiate others.The Societies piloted a webinar program in 2022 and are looking to expand it in 2023.The webinars are attracting hundreds of attendees and are a good way to promote the work of authors and the journal.Every author is invited to submit a short video summary for CFTM, but so far we have only published 3 (all of them in 2021).Especially for our applied journal, these video communications could really help set CFTM apart and reach our intended audience.In a similar way, graphical abstracts can be a useful tool for our applied audience, and we will begin asking for them in 2023.2022 was our first full year of inviting nearly every author to turn their CFTM article into a self-study article that would appear in front of 13,000+ CCAs who could then earn Continuing Education Units on it towards their certification.We invite authors to write a quiz in exchange for having page fees waived.Of the 49 authors we asked, 13 said yes.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.013 |
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