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
<strong class="journal-contentHeaderColor">Abstract.</strong> In the coming decades, a changing climate, the loss of high-quality land, the slowing in the annual yield of cereals, and increasing fertilizer use indicate that better agricultural water management strategies are needed. In this study, we designed FarmCan, a novel, robust remote sensing and machine learning (ML) framework to forecast farms' needed daily crop water quantity or needed irrigation (NI). We used a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with a random forest (RF) algorithm and inputs about farm-specific situations to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM). Our case study of four farms in the Canadian Prairies Ecozone (CPE) shows that 8âd composite precipitation (<span class="inline-formula"><i>P</i></span>) has the highest correlation with changes (<span class="inline-formula">Î</span>) of RZSM and SM. In contrast, 8âd PET and 8âd ET do not offer a strong correlation with 8âd <span class="inline-formula"><i>P</i></span>. Using <span class="inline-formula"><i>R</i><sup>2</sup></span>, root mean square error (RMSE), and KlingâGupta efficiency (KGE) indicators, our algorithm could reasonably calculate daily NI up to 14âd in advance. From 2015 to 2020, the <span class="inline-formula"><i>R</i><sup>2</sup></span> values between predicted and observed 8âd ET and 8âd PET were the highest (80â% and 54â%, respectively). The 8âd NI also had an average <span class="inline-formula"><i>R</i><sup>2</sup></span> of 68%. The KGE of the 8âd ET and 8âd PET in four study farms showed an average of 0.71 and 0.50, respectively, with an average KGE of 0.62. FarmCan can be used in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government policy concerns.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.477 | 0.012 |
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